A tool for urban soundscape evaluation applying Support Vector Machines for developing a soundscape classification model.

PubMed

Torija, Antonio J; Ruiz, Diego P; Ramos-Ridao, Angel F

2014-06-01

To ensure appropriate soundscape management in urban environments, the urban-planning authorities need a range of tools that enable such a task to be performed. An essential step during the management of urban areas from a sound standpoint should be the evaluation of the soundscape in such an area. In this sense, it has been widely acknowledged that a subjective and acoustical categorization of a soundscape is the first step to evaluate it, providing a basis for designing or adapting it to match people's expectations as well. In this sense, this work proposes a model for automatic classification of urban soundscapes. This model is intended for the automatic classification of urban soundscapes based on underlying acoustical and perceptual criteria. Thus, this classification model is proposed to be used as a tool for a comprehensive urban soundscape evaluation. Because of the great complexity associated with the problem, two machine learning techniques, Support Vector Machines (SVM) and Support Vector Machines trained with Sequential Minimal Optimization (SMO), are implemented in developing model classification. The results indicate that the SMO model outperforms the SVM model in the specific task of soundscape classification. With the implementation of the SMO algorithm, the classification model achieves an outstanding performance (91.3% of instances correctly classified). © 2013 Elsevier B.V. All rights reserved.

Transportation Modes Classification Using Sensors on Smartphones.

PubMed

Fang, Shih-Hau; Liao, Hao-Hsiang; Fei, Yu-Xiang; Chen, Kai-Hsiang; Huang, Jen-Wei; Lu, Yu-Ding; Tsao, Yu

2016-08-19

This paper investigates the transportation and vehicular modes classification by using big data from smartphone sensors. The three types of sensors used in this paper include the accelerometer, magnetometer, and gyroscope. This study proposes improved features and uses three machine learning algorithms including decision trees, K-nearest neighbor, and support vector machine to classify the user's transportation and vehicular modes. In the experiments, we discussed and compared the performance from different perspectives including the accuracy for both modes, the executive time, and the model size. Results show that the proposed features enhance the accuracy, in which the support vector machine provides the best performance in classification accuracy whereas it consumes the largest prediction time. This paper also investigates the vehicle classification mode and compares the results with that of the transportation modes.

Transportation Modes Classification Using Sensors on Smartphones

PubMed Central

Fang, Shih-Hau; Liao, Hao-Hsiang; Fei, Yu-Xiang; Chen, Kai-Hsiang; Huang, Jen-Wei; Lu, Yu-Ding; Tsao, Yu

2016-01-01

This paper investigates the transportation and vehicular modes classification by using big data from smartphone sensors. The three types of sensors used in this paper include the accelerometer, magnetometer, and gyroscope. This study proposes improved features and uses three machine learning algorithms including decision trees, K-nearest neighbor, and support vector machine to classify the user’s transportation and vehicular modes. In the experiments, we discussed and compared the performance from different perspectives including the accuracy for both modes, the executive time, and the model size. Results show that the proposed features enhance the accuracy, in which the support vector machine provides the best performance in classification accuracy whereas it consumes the largest prediction time. This paper also investigates the vehicle classification mode and compares the results with that of the transportation modes. PMID:27548182

Signal detection using support vector machines in the presence of ultrasonic speckle

NASA Astrophysics Data System (ADS)

Kotropoulos, Constantine L.; Pitas, Ioannis

2002-04-01

Support Vector Machines are a general algorithm based on guaranteed risk bounds of statistical learning theory. They have found numerous applications, such as in classification of brain PET images, optical character recognition, object detection, face verification, text categorization and so on. In this paper we propose the use of support vector machines to segment lesions in ultrasound images and we assess thoroughly their lesion detection ability. We demonstrate that trained support vector machines with a Radial Basis Function kernel segment satisfactorily (unseen) ultrasound B-mode images as well as clinical ultrasonic images.

Predicting complications of percutaneous coronary intervention using a novel support vector method.

PubMed

Lee, Gyemin; Gurm, Hitinder S; Syed, Zeeshan

2013-01-01

To explore the feasibility of a novel approach using an augmented one-class learning algorithm to model in-laboratory complications of percutaneous coronary intervention (PCI). Data from the Blue Cross Blue Shield of Michigan Cardiovascular Consortium (BMC2) multicenter registry for the years 2007 and 2008 (n=41 016) were used to train models to predict 13 different in-laboratory PCI complications using a novel one-plus-class support vector machine (OP-SVM) algorithm. The performance of these models in terms of discrimination and calibration was compared to the performance of models trained using the following classification algorithms on BMC2 data from 2009 (n=20 289): logistic regression (LR), one-class support vector machine classification (OC-SVM), and two-class support vector machine classification (TC-SVM). For the OP-SVM and TC-SVM approaches, variants of the algorithms with cost-sensitive weighting were also considered. The OP-SVM algorithm and its cost-sensitive variant achieved the highest area under the receiver operating characteristic curve for the majority of the PCI complications studied (eight cases). Similar improvements were observed for the Hosmer-Lemeshow χ(2) value (seven cases) and the mean cross-entropy error (eight cases). The OP-SVM algorithm based on an augmented one-class learning problem improved discrimination and calibration across different PCI complications relative to LR and traditional support vector machine classification. Such an approach may have value in a broader range of clinical domains.

Predicting complications of percutaneous coronary intervention using a novel support vector method

PubMed Central

Lee, Gyemin; Gurm, Hitinder S; Syed, Zeeshan

2013-01-01

Objective To explore the feasibility of a novel approach using an augmented one-class learning algorithm to model in-laboratory complications of percutaneous coronary intervention (PCI). Materials and methods Data from the Blue Cross Blue Shield of Michigan Cardiovascular Consortium (BMC2) multicenter registry for the years 2007 and 2008 (n=41 016) were used to train models to predict 13 different in-laboratory PCI complications using a novel one-plus-class support vector machine (OP-SVM) algorithm. The performance of these models in terms of discrimination and calibration was compared to the performance of models trained using the following classification algorithms on BMC2 data from 2009 (n=20 289): logistic regression (LR), one-class support vector machine classification (OC-SVM), and two-class support vector machine classification (TC-SVM). For the OP-SVM and TC-SVM approaches, variants of the algorithms with cost-sensitive weighting were also considered. Results The OP-SVM algorithm and its cost-sensitive variant achieved the highest area under the receiver operating characteristic curve for the majority of the PCI complications studied (eight cases). Similar improvements were observed for the Hosmer–Lemeshow χ2 value (seven cases) and the mean cross-entropy error (eight cases). Conclusions The OP-SVM algorithm based on an augmented one-class learning problem improved discrimination and calibration across different PCI complications relative to LR and traditional support vector machine classification. Such an approach may have value in a broader range of clinical domains. PMID:23599229

Interpreting support vector machine models for multivariate group wise analysis in neuroimaging

PubMed Central

Gaonkar, Bilwaj; Shinohara, Russell T; Davatzikos, Christos

2015-01-01

Machine learning based classification algorithms like support vector machines (SVMs) have shown great promise for turning a high dimensional neuroimaging data into clinically useful decision criteria. However, tracing imaging based patterns that contribute significantly to classifier decisions remains an open problem. This is an issue of critical importance in imaging studies seeking to determine which anatomical or physiological imaging features contribute to the classifier’s decision, thereby allowing users to critically evaluate the findings of such machine learning methods and to understand disease mechanisms. The majority of published work addresses the question of statistical inference for support vector classification using permutation tests based on SVM weight vectors. Such permutation testing ignores the SVM margin, which is critical in SVM theory. In this work we emphasize the use of a statistic that explicitly accounts for the SVM margin and show that the null distributions associated with this statistic are asymptotically normal. Further, our experiments show that this statistic is a lot less conservative as compared to weight based permutation tests and yet specific enough to tease out multivariate patterns in the data. Thus, we can better understand the multivariate patterns that the SVM uses for neuroimaging based classification. PMID:26210913

An implementation of support vector machine on sentiment classification of movie reviews

NASA Astrophysics Data System (ADS)

Yulietha, I. M.; Faraby, S. A.; Adiwijaya; Widyaningtyas, W. C.

2018-03-01

With technological advances, all information about movie is available on the internet. If the information is processed properly, it will get the quality of the information. This research proposes to the classify sentiments on movie review documents. This research uses Support Vector Machine (SVM) method because it can classify high dimensional data in accordance with the data used in this research in the form of text. Support Vector Machine is a popular machine learning technique for text classification because it can classify by learning from a collection of documents that have been classified previously and can provide good result. Based on number of datasets, the 90-10 composition has the best result that is 85.6%. Based on SVM kernel, kernel linear with constant 1 has the best result that is 84.9%

Support vector machine based classification of fast Fourier transform spectroscopy of proteins

NASA Astrophysics Data System (ADS)

Lazarevic, Aleksandar; Pokrajac, Dragoljub; Marcano, Aristides; Melikechi, Noureddine

2009-02-01

Fast Fourier transform spectroscopy has proved to be a powerful method for study of the secondary structure of proteins since peak positions and their relative amplitude are affected by the number of hydrogen bridges that sustain this secondary structure. However, to our best knowledge, the method has not been used yet for identification of proteins within a complex matrix like a blood sample. The principal reason is the apparent similarity of protein infrared spectra with actual differences usually masked by the solvent contribution and other interactions. In this paper, we propose a novel machine learning based method that uses protein spectra for classification and identification of such proteins within a given sample. The proposed method uses principal component analysis (PCA) to identify most important linear combinations of original spectral components and then employs support vector machine (SVM) classification model applied on such identified combinations to categorize proteins into one of given groups. Our experiments have been performed on the set of four different proteins, namely: Bovine Serum Albumin, Leptin, Insulin-like Growth Factor 2 and Osteopontin. Our proposed method of applying principal component analysis along with support vector machines exhibits excellent classification accuracy when identifying proteins using their infrared spectra.

An assessment of support vector machines for land cover classification

USGS Publications Warehouse

Huang, C.; Davis, L.S.; Townshend, J.R.G.

2002-01-01

The support vector machine (SVM) is a group of theoretically superior machine learning algorithms. It was found competitive with the best available machine learning algorithms in classifying high-dimensional data sets. This paper gives an introduction to the theoretical development of the SVM and an experimental evaluation of its accuracy, stability and training speed in deriving land cover classifications from satellite images. The SVM was compared to three other popular classifiers, including the maximum likelihood classifier (MLC), neural network classifiers (NNC) and decision tree classifiers (DTC). The impacts of kernel configuration on the performance of the SVM and of the selection of training data and input variables on the four classifiers were also evaluated in this experiment.

CNN universal machine as classificaton platform: an art-like clustering algorithm.

PubMed

Bálya, David

2003-12-01

Fast and robust classification of feature vectors is a crucial task in a number of real-time systems. A cellular neural/nonlinear network universal machine (CNN-UM) can be very efficient as a feature detector. The next step is to post-process the results for object recognition. This paper shows how a robust classification scheme based on adaptive resonance theory (ART) can be mapped to the CNN-UM. Moreover, this mapping is general enough to include different types of feed-forward neural networks. The designed analogic CNN algorithm is capable of classifying the extracted feature vectors keeping the advantages of the ART networks, such as robust, plastic and fault-tolerant behaviors. An analogic algorithm is presented for unsupervised classification with tunable sensitivity and automatic new class creation. The algorithm is extended for supervised classification. The presented binary feature vector classification is implemented on the existing standard CNN-UM chips for fast classification. The experimental evaluation shows promising performance after 100% accuracy on the training set.

SVM Classifier - a comprehensive java interface for support vector machine classification of microarray data.

PubMed

Pirooznia, Mehdi; Deng, Youping

2006-12-12

Graphical user interface (GUI) software promotes novelty by allowing users to extend the functionality. SVM Classifier is a cross-platform graphical application that handles very large datasets well. The purpose of this study is to create a GUI application that allows SVM users to perform SVM training, classification and prediction. The GUI provides user-friendly access to state-of-the-art SVM methods embodied in the LIBSVM implementation of Support Vector Machine. We implemented the java interface using standard swing libraries. We used a sample data from a breast cancer study for testing classification accuracy. We achieved 100% accuracy in classification among the BRCA1-BRCA2 samples with RBF kernel of SVM. We have developed a java GUI application that allows SVM users to perform SVM training, classification and prediction. We have demonstrated that support vector machines can accurately classify genes into functional categories based upon expression data from DNA microarray hybridization experiments. Among the different kernel functions that we examined, the SVM that uses a radial basis kernel function provides the best performance. The SVM Classifier is available at http://mfgn.usm.edu/ebl/svm/.

T-ray relevant frequencies for osteosarcoma classification

NASA Astrophysics Data System (ADS)

Withayachumnankul, W.; Ferguson, B.; Rainsford, T.; Findlay, D.; Mickan, S. P.; Abbott, D.

2006-01-01

We investigate the classification of the T-ray response of normal human bone cells and human osteosarcoma cells, grown in culture. Given the magnitude and phase responses within a reliable spectral range as features for input vectors, a trained support vector machine can correctly classify the two cell types to some extent. Performance of the support vector machine is deteriorated by the curse of dimensionality, resulting from the comparatively large number of features in the input vectors. Feature subset selection methods are used to select only an optimal number of relevant features for inputs. As a result, an improvement in generalization performance is attainable, and the selected frequencies can be used for further describing different mechanisms of the cells, responding to T-rays. We demonstrate a consistent classification accuracy of 89.6%, while the only one fifth of the original features are retained in the data set.

Application of Classification Models to Pharyngeal High-Resolution Manometry

ERIC Educational Resources Information Center

Mielens, Jason D.; Hoffman, Matthew R.; Ciucci, Michelle R.; McCulloch, Timothy M.; Jiang, Jack J.

2012-01-01

Purpose: The authors present 3 methods of performing pattern recognition on spatiotemporal plots produced by pharyngeal high-resolution manometry (HRM). Method: Classification models, including the artificial neural networks (ANNs) multilayer perceptron (MLP) and learning vector quantization (LVQ), as well as support vector machines (SVM), were…

Predicting Flavonoid UGT Regioselectivity

PubMed Central

Jackson, Rhydon; Knisley, Debra; McIntosh, Cecilia; Pfeiffer, Phillip

2011-01-01

Machine learning was applied to a challenging and biologically significant protein classification problem: the prediction of avonoid UGT acceptor regioselectivity from primary sequence. Novel indices characterizing graphical models of residues were proposed and found to be widely distributed among existing amino acid indices and to cluster residues appropriately. UGT subsequences biochemically linked to regioselectivity were modeled as sets of index sequences. Several learning techniques incorporating these UGT models were compared with classifications based on standard sequence alignment scores. These techniques included an application of time series distance functions to protein classification. Time series distances defined on the index sequences were used in nearest neighbor and support vector machine classifiers. Additionally, Bayesian neural network classifiers were applied to the index sequences. The experiments identified improvements over the nearest neighbor and support vector machine classifications relying on standard alignment similarity scores, as well as strong correlations between specific subsequences and regioselectivities. PMID:21747849

The maximum vector-angular margin classifier and its fast training on large datasets using a core vector machine.

PubMed

Hu, Wenjun; Chung, Fu-Lai; Wang, Shitong

2012-03-01

Although pattern classification has been extensively studied in the past decades, how to effectively solve the corresponding training on large datasets is a problem that still requires particular attention. Many kernelized classification methods, such as SVM and SVDD, can be formulated as the corresponding quadratic programming (QP) problems, but computing the associated kernel matrices requires O(n2)(or even up to O(n3)) computational complexity, where n is the size of the training patterns, which heavily limits the applicability of these methods for large datasets. In this paper, a new classification method called the maximum vector-angular margin classifier (MAMC) is first proposed based on the vector-angular margin to find an optimal vector c in the pattern feature space, and all the testing patterns can be classified in terms of the maximum vector-angular margin ρ, between the vector c and all the training data points. Accordingly, it is proved that the kernelized MAMC can be equivalently formulated as the kernelized Minimum Enclosing Ball (MEB), which leads to a distinctive merit of MAMC, i.e., it has the flexibility of controlling the sum of support vectors like v-SVC and may be extended to a maximum vector-angular margin core vector machine (MAMCVM) by connecting the core vector machine (CVM) method with MAMC such that the corresponding fast training on large datasets can be effectively achieved. Experimental results on artificial and real datasets are provided to validate the power of the proposed methods. Copyright © 2011 Elsevier Ltd. All rights reserved.

Vision based nutrient deficiency classification in maize plants using multi class support vector machines

NASA Astrophysics Data System (ADS)

Leena, N.; Saju, K. K.

2018-04-01

Nutritional deficiencies in plants are a major concern for farmers as it affects productivity and thus profit. The work aims to classify nutritional deficiencies in maize plant in a non-destructive mannerusing image processing and machine learning techniques. The colored images of the leaves are analyzed and classified with multi-class support vector machine (SVM) method. Several images of maize leaves with known deficiencies like nitrogen, phosphorous and potassium (NPK) are used to train the SVM classifier prior to the classification of test images. The results show that the method was able to classify and identify nutritional deficiencies.

Support Vector Machines Trained with Evolutionary Algorithms Employing Kernel Adatron for Large Scale Classification of Protein Structures.

PubMed

Arana-Daniel, Nancy; Gallegos, Alberto A; López-Franco, Carlos; Alanís, Alma Y; Morales, Jacob; López-Franco, Adriana

2016-01-01

With the increasing power of computers, the amount of data that can be processed in small periods of time has grown exponentially, as has the importance of classifying large-scale data efficiently. Support vector machines have shown good results classifying large amounts of high-dimensional data, such as data generated by protein structure prediction, spam recognition, medical diagnosis, optical character recognition and text classification, etc. Most state of the art approaches for large-scale learning use traditional optimization methods, such as quadratic programming or gradient descent, which makes the use of evolutionary algorithms for training support vector machines an area to be explored. The present paper proposes an approach that is simple to implement based on evolutionary algorithms and Kernel-Adatron for solving large-scale classification problems, focusing on protein structure prediction. The functional properties of proteins depend upon their three-dimensional structures. Knowing the structures of proteins is crucial for biology and can lead to improvements in areas such as medicine, agriculture and biofuels.

Extraction of inland Nypa fruticans (Nipa Palm) using Support Vector Machine

NASA Astrophysics Data System (ADS)

Alberto, R. T.; Serrano, S. C.; Damian, G. B.; Camaso, E. E.; Biagtan, A. R.; Panuyas, N. Z.; Quibuyen, J. S.

2017-09-01

Mangroves are considered as one of the major habitats in coastal ecosystem, providing a lot of economic and ecological services in human society. Nypa fruticans (Nipa palm) is one of the important species of mangroves because of its versatility and uniqueness as halophytic palm. However, nipas are not only adaptable in saline areas, they can also managed to thrive away from the coastline depending on the favorable soil types available in the area. Because of this, mapping of this species are not limited alone in the near shore areas, but in areas where this species are present as well. The extraction process of Nypa fruticans were carried out using the available LiDAR data. Support Vector Machine (SVM) classification process was used to extract nipas in inland areas. The SVM classification process in mapping Nypa fruticans produced high accuracy of 95+%. The Support Vector Machine classification process to extract inland nipas was proven to be effective by utilizing different terrain derivatives from LiDAR data.

Using an object-based grid system to evaluate a newly developed EP approach to formulate SVMs as applied to the classification of organophosphate nerve agents

NASA Astrophysics Data System (ADS)

Land, Walker H., Jr.; Lewis, Michael; Sadik, Omowunmi; Wong, Lut; Wanekaya, Adam; Gonzalez, Richard J.; Balan, Arun

2004-04-01

This paper extends the classification approaches described in reference [1] in the following way: (1.) developing and evaluating a new method for evolving organophosphate nerve agent Support Vector Machine (SVM) classifiers using Evolutionary Programming, (2.) conducting research experiments using a larger database of organophosphate nerve agents, and (3.) upgrading the architecture to an object-based grid system for evaluating the classification of EP derived SVMs. Due to the increased threats of chemical and biological weapons of mass destruction (WMD) by international terrorist organizations, a significant effort is underway to develop tools that can be used to detect and effectively combat biochemical warfare. This paper reports the integration of multi-array sensors with Support Vector Machines (SVMs) for the detection of organophosphates nerve agents using a grid computing system called Legion. Grid computing is the use of large collections of heterogeneous, distributed resources (including machines, databases, devices, and users) to support large-scale computations and wide-area data access. Finally, preliminary results using EP derived support vector machines designed to operate on distributed systems have provided accurate classification results. In addition, distributed training time architectures are 50 times faster when compared to standard iterative training time methods.

Analysis of spectrally resolved autofluorescence images by support vector machines

NASA Astrophysics Data System (ADS)

Mateasik, A.; Chorvat, D.; Chorvatova, A.

2013-02-01

Spectral analysis of the autofluorescence images of isolated cardiac cells was performed to evaluate and to classify the metabolic state of the cells in respect to the responses to metabolic modulators. The classification was done using machine learning approach based on support vector machine with the set of the automatically calculated features from recorded spectral profile of spectral autofluorescence images. This classification method was compared with the classical approach where the individual spectral components contributing to cell autofluorescence were estimated by spectral analysis, namely by blind source separation using non-negative matrix factorization. Comparison of both methods showed that machine learning can effectively classify the spectrally resolved autofluorescence images without the need of detailed knowledge about the sources of autofluorescence and their spectral properties.

Gender classification of running subjects using full-body kinematics

NASA Astrophysics Data System (ADS)

Williams, Christina M.; Flora, Jeffrey B.; Iftekharuddin, Khan M.

2016-05-01

This paper proposes novel automated gender classification of subjects while engaged in running activity. The machine learning techniques include preprocessing steps using principal component analysis followed by classification with linear discriminant analysis, and nonlinear support vector machines, and decision-stump with AdaBoost. The dataset consists of 49 subjects (25 males, 24 females, 2 trials each) all equipped with approximately 80 retroreflective markers. The trials are reflective of the subject's entire body moving unrestrained through a capture volume at a self-selected running speed, thus producing highly realistic data. The classification accuracy using leave-one-out cross validation for the 49 subjects is improved from 66.33% using linear discriminant analysis to 86.74% using the nonlinear support vector machine. Results are further improved to 87.76% by means of implementing a nonlinear decision stump with AdaBoost classifier. The experimental findings suggest that the linear classification approaches are inadequate in classifying gender for a large dataset with subjects running in a moderately uninhibited environment.

Machine Learning for Biological Trajectory Classification Applications

NASA Technical Reports Server (NTRS)

Sbalzarini, Ivo F.; Theriot, Julie; Koumoutsakos, Petros

2002-01-01

Machine-learning techniques, including clustering algorithms, support vector machines and hidden Markov models, are applied to the task of classifying trajectories of moving keratocyte cells. The different algorithms axe compared to each other as well as to expert and non-expert test persons, using concepts from signal-detection theory. The algorithms performed very well as compared to humans, suggesting a robust tool for trajectory classification in biological applications.

Combined empirical mode decomposition and texture features for skin lesion classification using quadratic support vector machine.

PubMed

Wahba, Maram A; Ashour, Amira S; Napoleon, Sameh A; Abd Elnaby, Mustafa M; Guo, Yanhui

2017-12-01

Basal cell carcinoma is one of the most common malignant skin lesions. Automated lesion identification and classification using image processing techniques is highly required to reduce the diagnosis errors. In this study, a novel technique is applied to classify skin lesion images into two classes, namely the malignant Basal cell carcinoma and the benign nevus. A hybrid combination of bi-dimensional empirical mode decomposition and gray-level difference method features is proposed after hair removal. The combined features are further classified using quadratic support vector machine (Q-SVM). The proposed system has achieved outstanding performance of 100% accuracy, sensitivity and specificity compared to other support vector machine procedures as well as with different extracted features. Basal Cell Carcinoma is effectively classified using Q-SVM with the proposed combined features.

Regularised extreme learning machine with misclassification cost and rejection cost for gene expression data classification.

PubMed

Lu, Huijuan; Wei, Shasha; Zhou, Zili; Miao, Yanzi; Lu, Yi

2015-01-01

The main purpose of traditional classification algorithms on bioinformatics application is to acquire better classification accuracy. However, these algorithms cannot meet the requirement that minimises the average misclassification cost. In this paper, a new algorithm of cost-sensitive regularised extreme learning machine (CS-RELM) was proposed by using probability estimation and misclassification cost to reconstruct the classification results. By improving the classification accuracy of a group of small sample which higher misclassification cost, the new CS-RELM can minimise the classification cost. The 'rejection cost' was integrated into CS-RELM algorithm to further reduce the average misclassification cost. By using Colon Tumour dataset and SRBCT (Small Round Blue Cells Tumour) dataset, CS-RELM was compared with other cost-sensitive algorithms such as extreme learning machine (ELM), cost-sensitive extreme learning machine, regularised extreme learning machine, cost-sensitive support vector machine (SVM). The results of experiments show that CS-RELM with embedded rejection cost could reduce the average cost of misclassification and made more credible classification decision than others.

Diagnosis of Chronic Kidney Disease Based on Support Vector Machine by Feature Selection Methods.

PubMed

Polat, Huseyin; Danaei Mehr, Homay; Cetin, Aydin

2017-04-01

As Chronic Kidney Disease progresses slowly, early detection and effective treatment are the only cure to reduce the mortality rate. Machine learning techniques are gaining significance in medical diagnosis because of their classification ability with high accuracy rates. The accuracy of classification algorithms depend on the use of correct feature selection algorithms to reduce the dimension of datasets. In this study, Support Vector Machine classification algorithm was used to diagnose Chronic Kidney Disease. To diagnose the Chronic Kidney Disease, two essential types of feature selection methods namely, wrapper and filter approaches were chosen to reduce the dimension of Chronic Kidney Disease dataset. In wrapper approach, classifier subset evaluator with greedy stepwise search engine and wrapper subset evaluator with the Best First search engine were used. In filter approach, correlation feature selection subset evaluator with greedy stepwise search engine and filtered subset evaluator with the Best First search engine were used. The results showed that the Support Vector Machine classifier by using filtered subset evaluator with the Best First search engine feature selection method has higher accuracy rate (98.5%) in the diagnosis of Chronic Kidney Disease compared to other selected methods.

A comparative study of machine learning models for ethnicity classification

NASA Astrophysics Data System (ADS)

Trivedi, Advait; Bessie Amali, D. Geraldine

2017-11-01

This paper endeavours to adopt a machine learning approach to solve the problem of ethnicity recognition. Ethnicity identification is an important vision problem with its use cases being extended to various domains. Despite the multitude of complexity involved, ethnicity identification comes naturally to humans. This meta information can be leveraged to make several decisions, be it in target marketing or security. With the recent development of intelligent systems a sub module to efficiently capture ethnicity would be useful in several use cases. Several attempts to identify an ideal learning model to represent a multi-ethnic dataset have been recorded. A comparative study of classifiers such as support vector machines, logistic regression has been documented. Experimental results indicate that the logical classifier provides a much accurate classification than the support vector machine.

A new method for the prediction of chatter stability lobes based on dynamic cutting force simulation model and support vector machine

NASA Astrophysics Data System (ADS)

Peng, Chong; Wang, Lun; Liao, T. Warren

2015-10-01

Currently, chatter has become the critical factor in hindering machining quality and productivity in machining processes. To avoid cutting chatter, a new method based on dynamic cutting force simulation model and support vector machine (SVM) is presented for the prediction of chatter stability lobes. The cutting force is selected as the monitoring signal, and the wavelet energy entropy theory is used to extract the feature vectors. A support vector machine is constructed using the MATLAB LIBSVM toolbox for pattern classification based on the feature vectors derived from the experimental cutting data. Then combining with the dynamic cutting force simulation model, the stability lobes diagram (SLD) can be estimated. Finally, the predicted results are compared with existing methods such as zero-order analytical (ZOA) and semi-discretization (SD) method as well as actual cutting experimental results to confirm the validity of this new method.

Protein Kinase Classification with 2866 Hidden Markov Models and One Support Vector Machine

NASA Technical Reports Server (NTRS)

Weber, Ryan; New, Michael H.; Fonda, Mark (Technical Monitor)

2002-01-01

The main application considered in this paper is predicting true kinases from randomly permuted kinases that share the same length and amino acid distributions as the true kinases. Numerous methods already exist for this classification task, such as HMMs, motif-matchers, and sequence comparison algorithms. We build on some of these efforts by creating a vector from the output of thousands of structurally based HMMs, created offline with Pfam-A seed alignments using SAM-T99, which then must be combined into an overall classification for the protein. Then we use a Support Vector Machine for classifying this large ensemble Pfam-Vector, with a polynomial and chisquared kernel. In particular, the chi-squared kernel SVM performs better than the HMMs and better than the BLAST pairwise comparisons, when predicting true from false kinases in some respects, but no one algorithm is best for all purposes or in all instances so we consider the particular strengths and weaknesses of each.

Clifford support vector machines for classification, regression, and recurrence.

PubMed

Bayro-Corrochano, Eduardo Jose; Arana-Daniel, Nancy

2010-11-01

This paper introduces the Clifford support vector machines (CSVM) as a generalization of the real and complex-valued support vector machines using the Clifford geometric algebra. In this framework, we handle the design of kernels involving the Clifford or geometric product. In this approach, one redefines the optimization variables as multivectors. This allows us to have a multivector as output. Therefore, we can represent multiple classes according to the dimension of the geometric algebra in which we work. We show that one can apply CSVM for classification and regression and also to build a recurrent CSVM. The CSVM is an attractive approach for the multiple input multiple output processing of high-dimensional geometric entities. We carried out comparisons between CSVM and the current approaches to solve multiclass classification and regression. We also study the performance of the recurrent CSVM with experiments involving time series. The authors believe that this paper can be of great use for researchers and practitioners interested in multiclass hypercomplex computing, particularly for applications in complex and quaternion signal and image processing, satellite control, neurocomputation, pattern recognition, computer vision, augmented virtual reality, robotics, and humanoids.

The construction of support vector machine classifier using the firefly algorithm.

PubMed

Chao, Chih-Feng; Horng, Ming-Huwi

2015-01-01

The setting of parameters in the support vector machines (SVMs) is very important with regard to its accuracy and efficiency. In this paper, we employ the firefly algorithm to train all parameters of the SVM simultaneously, including the penalty parameter, smoothness parameter, and Lagrangian multiplier. The proposed method is called the firefly-based SVM (firefly-SVM). This tool is not considered the feature selection, because the SVM, together with feature selection, is not suitable for the application in a multiclass classification, especially for the one-against-all multiclass SVM. In experiments, binary and multiclass classifications are explored. In the experiments on binary classification, ten of the benchmark data sets of the University of California, Irvine (UCI), machine learning repository are used; additionally the firefly-SVM is applied to the multiclass diagnosis of ultrasonic supraspinatus images. The classification performance of firefly-SVM is also compared to the original LIBSVM method associated with the grid search method and the particle swarm optimization based SVM (PSO-SVM). The experimental results advocate the use of firefly-SVM to classify pattern classifications for maximum accuracy.

The Construction of Support Vector Machine Classifier Using the Firefly Algorithm

PubMed Central

Chao, Chih-Feng; Horng, Ming-Huwi

2015-01-01

The setting of parameters in the support vector machines (SVMs) is very important with regard to its accuracy and efficiency. In this paper, we employ the firefly algorithm to train all parameters of the SVM simultaneously, including the penalty parameter, smoothness parameter, and Lagrangian multiplier. The proposed method is called the firefly-based SVM (firefly-SVM). This tool is not considered the feature selection, because the SVM, together with feature selection, is not suitable for the application in a multiclass classification, especially for the one-against-all multiclass SVM. In experiments, binary and multiclass classifications are explored. In the experiments on binary classification, ten of the benchmark data sets of the University of California, Irvine (UCI), machine learning repository are used; additionally the firefly-SVM is applied to the multiclass diagnosis of ultrasonic supraspinatus images. The classification performance of firefly-SVM is also compared to the original LIBSVM method associated with the grid search method and the particle swarm optimization based SVM (PSO-SVM). The experimental results advocate the use of firefly-SVM to classify pattern classifications for maximum accuracy. PMID:25802511

Data mining methods in the prediction of Dementia: A real-data comparison of the accuracy, sensitivity and specificity of linear discriminant analysis, logistic regression, neural networks, support vector machines, classification trees and random forests

PubMed Central

2011-01-01

Background Dementia and cognitive impairment associated with aging are a major medical and social concern. Neuropsychological testing is a key element in the diagnostic procedures of Mild Cognitive Impairment (MCI), but has presently a limited value in the prediction of progression to dementia. We advance the hypothesis that newer statistical classification methods derived from data mining and machine learning methods like Neural Networks, Support Vector Machines and Random Forests can improve accuracy, sensitivity and specificity of predictions obtained from neuropsychological testing. Seven non parametric classifiers derived from data mining methods (Multilayer Perceptrons Neural Networks, Radial Basis Function Neural Networks, Support Vector Machines, CART, CHAID and QUEST Classification Trees and Random Forests) were compared to three traditional classifiers (Linear Discriminant Analysis, Quadratic Discriminant Analysis and Logistic Regression) in terms of overall classification accuracy, specificity, sensitivity, Area under the ROC curve and Press'Q. Model predictors were 10 neuropsychological tests currently used in the diagnosis of dementia. Statistical distributions of classification parameters obtained from a 5-fold cross-validation were compared using the Friedman's nonparametric test. Results Press' Q test showed that all classifiers performed better than chance alone (p < 0.05). Support Vector Machines showed the larger overall classification accuracy (Median (Me) = 0.76) an area under the ROC (Me = 0.90). However this method showed high specificity (Me = 1.0) but low sensitivity (Me = 0.3). Random Forest ranked second in overall accuracy (Me = 0.73) with high area under the ROC (Me = 0.73) specificity (Me = 0.73) and sensitivity (Me = 0.64). Linear Discriminant Analysis also showed acceptable overall accuracy (Me = 0.66), with acceptable area under the ROC (Me = 0.72) specificity (Me = 0.66) and sensitivity (Me = 0.64). The remaining classifiers showed overall classification accuracy above a median value of 0.63, but for most sensitivity was around or even lower than a median value of 0.5. Conclusions When taking into account sensitivity, specificity and overall classification accuracy Random Forests and Linear Discriminant analysis rank first among all the classifiers tested in prediction of dementia using several neuropsychological tests. These methods may be used to improve accuracy, sensitivity and specificity of Dementia predictions from neuropsychological testing. PMID:21849043

A low cost implementation of multi-parameter patient monitor using intersection kernel support vector machine classifier

NASA Astrophysics Data System (ADS)

Mohan, Dhanya; Kumar, C. Santhosh

2016-03-01

Predicting the physiological condition (normal/abnormal) of a patient is highly desirable to enhance the quality of health care. Multi-parameter patient monitors (MPMs) using heart rate, arterial blood pressure, respiration rate and oxygen saturation (S pO2) as input parameters were developed to monitor the condition of patients, with minimum human resource utilization. The Support vector machine (SVM), an advanced machine learning approach popularly used for classification and regression is used for the realization of MPMs. For making MPMs cost effective, we experiment on the hardware implementation of the MPM using support vector machine classifier. The training of the system is done using the matlab environment and the detection of the alarm/noalarm condition is implemented in hardware. We used different kernels for SVM classification and note that the best performance was obtained using intersection kernel SVM (IKSVM). The intersection kernel support vector machine classifier MPM has outperformed the best known MPM using radial basis function kernel by an absoute improvement of 2.74% in accuracy, 1.86% in sensitivity and 3.01% in specificity. The hardware model was developed based on the improved performance system using Verilog Hardware Description Language and was implemented on Altera cyclone-II development board.

Multivariate analysis of fMRI time series: classification and regression of brain responses using machine learning.

PubMed

Formisano, Elia; De Martino, Federico; Valente, Giancarlo

2008-09-01

Machine learning and pattern recognition techniques are being increasingly employed in functional magnetic resonance imaging (fMRI) data analysis. By taking into account the full spatial pattern of brain activity measured simultaneously at many locations, these methods allow detecting subtle, non-strictly localized effects that may remain invisible to the conventional analysis with univariate statistical methods. In typical fMRI applications, pattern recognition algorithms "learn" a functional relationship between brain response patterns and a perceptual, cognitive or behavioral state of a subject expressed in terms of a label, which may assume discrete (classification) or continuous (regression) values. This learned functional relationship is then used to predict the unseen labels from a new data set ("brain reading"). In this article, we describe the mathematical foundations of machine learning applications in fMRI. We focus on two methods, support vector machines and relevance vector machines, which are respectively suited for the classification and regression of fMRI patterns. Furthermore, by means of several examples and applications, we illustrate and discuss the methodological challenges of using machine learning algorithms in the context of fMRI data analysis.

Implementation of support vector machine for classification of speech marked hijaiyah letters based on Mel frequency cepstrum coefficient feature extraction

NASA Astrophysics Data System (ADS)

Adhi Pradana, Wisnu; Adiwijaya; Novia Wisesty, Untari

2018-03-01

Support Vector Machine or commonly called SVM is one method that can be used to process the classification of a data. SVM classifies data from 2 different classes with hyperplane. In this study, the system was built using SVM to develop Arabic Speech Recognition. In the development of the system, there are 2 kinds of speakers that have been tested that is dependent speakers and independent speakers. The results from this system is an accuracy of 85.32% for speaker dependent and 61.16% for independent speakers.

Support-vector-machine tree-based domain knowledge learning toward automated sports video classification

NASA Astrophysics Data System (ADS)

Xiao, Guoqiang; Jiang, Yang; Song, Gang; Jiang, Jianmin

2010-12-01

We propose a support-vector-machine (SVM) tree to hierarchically learn from domain knowledge represented by low-level features toward automatic classification of sports videos. The proposed SVM tree adopts a binary tree structure to exploit the nature of SVM's binary classification, where each internal node is a single SVM learning unit, and each external node represents the classified output type. Such a SVM tree presents a number of advantages, which include: 1. low computing cost; 2. integrated learning and classification while preserving individual SVM's learning strength; and 3. flexibility in both structure and learning modules, where different numbers of nodes and features can be added to address specific learning requirements, and various learning models can be added as individual nodes, such as neural networks, AdaBoost, hidden Markov models, dynamic Bayesian networks, etc. Experiments support that the proposed SVM tree achieves good performances in sports video classifications.

Progressive Classification Using Support Vector Machines

NASA Technical Reports Server (NTRS)

Wagstaff, Kiri; Kocurek, Michael

2009-01-01

An algorithm for progressive classification of data, analogous to progressive rendering of images, makes it possible to compromise between speed and accuracy. This algorithm uses support vector machines (SVMs) to classify data. An SVM is a machine learning algorithm that builds a mathematical model of the desired classification concept by identifying the critical data points, called support vectors. Coarse approximations to the concept require only a few support vectors, while precise, highly accurate models require far more support vectors. Once the model has been constructed, the SVM can be applied to new observations. The cost of classifying a new observation is proportional to the number of support vectors in the model. When computational resources are limited, an SVM of the appropriate complexity can be produced. However, if the constraints are not known when the model is constructed, or if they can change over time, a method for adaptively responding to the current resource constraints is required. This capability is particularly relevant for spacecraft (or any other real-time systems) that perform onboard data analysis. The new algorithm enables the fast, interactive application of an SVM classifier to a new set of data. The classification process achieved by this algorithm is characterized as progressive because a coarse approximation to the true classification is generated rapidly and thereafter iteratively refined. The algorithm uses two SVMs: (1) a fast, approximate one and (2) slow, highly accurate one. New data are initially classified by the fast SVM, producing a baseline approximate classification. For each classified data point, the algorithm calculates a confidence index that indicates the likelihood that it was classified correctly in the first pass. Next, the data points are sorted by their confidence indices and progressively reclassified by the slower, more accurate SVM, starting with the items most likely to be incorrectly classified. The user can halt this reclassification process at any point, thereby obtaining the best possible result for a given amount of computation time. Alternatively, the results can be displayed as they are generated, providing the user with real-time feedback about the current accuracy of classification.

Vowel Imagery Decoding toward Silent Speech BCI Using Extreme Learning Machine with Electroencephalogram

PubMed Central

Kim, Jongin; Park, Hyeong-jun

2016-01-01

The purpose of this study is to classify EEG data on imagined speech in a single trial. We recorded EEG data while five subjects imagined different vowels, /a/, /e/, /i/, /o/, and /u/. We divided each single trial dataset into thirty segments and extracted features (mean, variance, standard deviation, and skewness) from all segments. To reduce the dimension of the feature vector, we applied a feature selection algorithm based on the sparse regression model. These features were classified using a support vector machine with a radial basis function kernel, an extreme learning machine, and two variants of an extreme learning machine with different kernels. Because each single trial consisted of thirty segments, our algorithm decided the label of the single trial by selecting the most frequent output among the outputs of the thirty segments. As a result, we observed that the extreme learning machine and its variants achieved better classification rates than the support vector machine with a radial basis function kernel and linear discrimination analysis. Thus, our results suggested that EEG responses to imagined speech could be successfully classified in a single trial using an extreme learning machine with a radial basis function and linear kernel. This study with classification of imagined speech might contribute to the development of silent speech BCI systems. PMID:28097128

Full-motion video analysis for improved gender classification

NASA Astrophysics Data System (ADS)

Flora, Jeffrey B.; Lochtefeld, Darrell F.; Iftekharuddin, Khan M.

2014-06-01

The ability of computer systems to perform gender classification using the dynamic motion of the human subject has important applications in medicine, human factors, and human-computer interface systems. Previous works in motion analysis have used data from sensors (including gyroscopes, accelerometers, and force plates), radar signatures, and video. However, full-motion video, motion capture, range data provides a higher resolution time and spatial dataset for the analysis of dynamic motion. Works using motion capture data have been limited by small datasets in a controlled environment. In this paper, we explore machine learning techniques to a new dataset that has a larger number of subjects. Additionally, these subjects move unrestricted through a capture volume, representing a more realistic, less controlled environment. We conclude that existing linear classification methods are insufficient for the gender classification for larger dataset captured in relatively uncontrolled environment. A method based on a nonlinear support vector machine classifier is proposed to obtain gender classification for the larger dataset. In experimental testing with a dataset consisting of 98 trials (49 subjects, 2 trials per subject), classification rates using leave-one-out cross-validation are improved from 73% using linear discriminant analysis to 88% using the nonlinear support vector machine classifier.

Multi-centre diagnostic classification of individual structural neuroimaging scans from patients with major depressive disorder.

PubMed

Mwangi, Benson; Ebmeier, Klaus P; Matthews, Keith; Steele, J Douglas

2012-05-01

Quantitative abnormalities of brain structure in patients with major depressive disorder have been reported at a group level for decades. However, these structural differences appear subtle in comparison with conventional radiologically defined abnormalities, with considerable inter-subject variability. Consequently, it has not been possible to readily identify scans from patients with major depressive disorder at an individual level. Recently, machine learning techniques such as relevance vector machines and support vector machines have been applied to predictive classification of individual scans with variable success. Here we describe a novel hybrid method, which combines machine learning with feature selection and characterization, with the latter aimed at maximizing the accuracy of machine learning prediction. The method was tested using a multi-centre dataset of T(1)-weighted 'structural' scans. A total of 62 patients with major depressive disorder and matched controls were recruited from referred secondary care clinical populations in Aberdeen and Edinburgh, UK. The generalization ability and predictive accuracy of the classifiers was tested using data left out of the training process. High prediction accuracy was achieved (~90%). While feature selection was important for maximizing high predictive accuracy with machine learning, feature characterization contributed only a modest improvement to relevance vector machine-based prediction (~5%). Notably, while the only information provided for training the classifiers was T(1)-weighted scans plus a categorical label (major depressive disorder versus controls), both relevance vector machine and support vector machine 'weighting factors' (used for making predictions) correlated strongly with subjective ratings of illness severity. These results indicate that machine learning techniques have the potential to inform clinical practice and research, as they can make accurate predictions about brain scan data from individual subjects. Furthermore, machine learning weighting factors may reflect an objective biomarker of major depressive disorder illness severity, based on abnormalities of brain structure.

Support Vector Machines for Hyperspectral Remote Sensing Classification

NASA Technical Reports Server (NTRS)

Gualtieri, J. Anthony; Cromp, R. F.

1998-01-01

The Support Vector Machine provides a new way to design classification algorithms which learn from examples (supervised learning) and generalize when applied to new data. We demonstrate its success on a difficult classification problem from hyperspectral remote sensing, where we obtain performances of 96%, and 87% correct for a 4 class problem, and a 16 class problem respectively. These results are somewhat better than other recent results on the same data. A key feature of this classifier is its ability to use high-dimensional data without the usual recourse to a feature selection step to reduce the dimensionality of the data. For this application, this is important, as hyperspectral data consists of several hundred contiguous spectral channels for each exemplar. We provide an introduction to this new approach, and demonstrate its application to classification of an agriculture scene.

Salient Feature Identification and Analysis using Kernel-Based Classification Techniques for Synthetic Aperture Radar Automatic Target Recognition

DTIC Science & Technology

2014-03-27

and machine learning for a range of research including such topics as medical imaging [10] and handwriting recognition [11]. The type of feature...1989. [11] C. Bahlmann, B. Haasdonk, and H. Burkhardt, “Online handwriting recognition with support vector machines-a kernel approach,” in Eighth...International Workshop on Frontiers in Handwriting Recognition, pp. 49–54, IEEE, 2002. [12] C. Cortes and V. Vapnik, “Support-vector networks,” Machine

Per-field crop classification in irrigated agricultural regions in middle Asia using random forest and support vector machine ensemble

NASA Astrophysics Data System (ADS)

Löw, Fabian; Schorcht, Gunther; Michel, Ulrich; Dech, Stefan; Conrad, Christopher

2012-10-01

Accurate crop identification and crop area estimation are important for studies on irrigated agricultural systems, yield and water demand modeling, and agrarian policy development. In this study a novel combination of Random Forest (RF) and Support Vector Machine (SVM) classifiers is presented that (i) enhances crop classification accuracy and (ii) provides spatial information on map uncertainty. The methodology was implemented over four distinct irrigated sites in Middle Asia using RapidEye time series data. The RF feature importance statistics was used as feature-selection strategy for the SVM to assess possible negative effects on classification accuracy caused by an oversized feature space. The results of the individual RF and SVM classifications were combined with rules based on posterior classification probability and estimates of classification probability entropy. SVM classification performance was increased by feature selection through RF. Further experimental results indicate that the hybrid classifier improves overall classification accuracy in comparison to the single classifiers as well as useŕs and produceŕs accuracy.

Optimization of Support Vector Machine (SVM) for Object Classification

NASA Technical Reports Server (NTRS)

Scholten, Matthew; Dhingra, Neil; Lu, Thomas T.; Chao, Tien-Hsin

2012-01-01

The Support Vector Machine (SVM) is a powerful algorithm, useful in classifying data into species. The SVMs implemented in this research were used as classifiers for the final stage in a Multistage Automatic Target Recognition (ATR) system. A single kernel SVM known as SVMlight, and a modified version known as a SVM with K-Means Clustering were used. These SVM algorithms were tested as classifiers under varying conditions. Image noise levels varied, and the orientation of the targets changed. The classifiers were then optimized to demonstrate their maximum potential as classifiers. Results demonstrate the reliability of SVM as a method for classification. From trial to trial, SVM produces consistent results.

Stroke localization and classification using microwave tomography with k-means clustering and support vector machine.

PubMed

Guo, Lei; Abbosh, Amin

2018-05-01

For any chance for stroke patients to survive, the stroke type should be classified to enable giving medication within a few hours of the onset of symptoms. In this paper, a microwave-based stroke localization and classification framework is proposed. It is based on microwave tomography, k-means clustering, and a support vector machine (SVM) method. The dielectric profile of the brain is first calculated using the Born iterative method, whereas the amplitude of the dielectric profile is then taken as the input to k-means clustering. The cluster is selected as the feature vector for constructing and testing the SVM. A database of MRI-derived realistic head phantoms at different signal-to-noise ratios is used in the classification procedure. The performance of the proposed framework is evaluated using the receiver operating characteristic (ROC) curve. The results based on a two-dimensional framework show that 88% classification accuracy, with a sensitivity of 91% and a specificity of 87%, can be achieved. Bioelectromagnetics. 39:312-324, 2018. © 2018 Wiley Periodicals, Inc. © 2018 Wiley Periodicals, Inc.

Optimizing support vector machine learning for semi-arid vegetation mapping by using clustering analysis

NASA Astrophysics Data System (ADS)

Su, Lihong

In remote sensing communities, support vector machine (SVM) learning has recently received increasing attention. SVM learning usually requires large memory and enormous amounts of computation time on large training sets. According to SVM algorithms, the SVM classification decision function is fully determined by support vectors, which compose a subset of the training sets. In this regard, a solution to optimize SVM learning is to efficiently reduce training sets. In this paper, a data reduction method based on agglomerative hierarchical clustering is proposed to obtain smaller training sets for SVM learning. Using a multiple angle remote sensing dataset of a semi-arid region, the effectiveness of the proposed method is evaluated by classification experiments with a series of reduced training sets. The experiments show that there is no loss of SVM accuracy when the original training set is reduced to 34% using the proposed approach. Maximum likelihood classification (MLC) also is applied on the reduced training sets. The results show that MLC can also maintain the classification accuracy. This implies that the most informative data instances can be retained by this approach.

Feature selection using a one dimensional naïve Bayes' classifier increases the accuracy of support vector machine classification of CDR3 repertoires.

PubMed

Cinelli, Mattia; Sun, Yuxin; Best, Katharine; Heather, James M; Reich-Zeliger, Shlomit; Shifrut, Eric; Friedman, Nir; Shawe-Taylor, John; Chain, Benny

2017-04-01

Somatic DNA recombination, the hallmark of vertebrate adaptive immunity, has the potential to generate a vast diversity of antigen receptor sequences. How this diversity captures antigen specificity remains incompletely understood. In this study we use high throughput sequencing to compare the global changes in T cell receptor β chain complementarity determining region 3 (CDR3β) sequences following immunization with ovalbumin administered with complete Freund's adjuvant (CFA) or CFA alone. The CDR3β sequences were deconstructed into short stretches of overlapping contiguous amino acids. The motifs were ranked according to a one-dimensional Bayesian classifier score comparing their frequency in the repertoires of the two immunization classes. The top ranking motifs were selected and used to create feature vectors which were used to train a support vector machine. The support vector machine achieved high classification scores in a leave-one-out validation test reaching >90% in some cases. The study describes a novel two-stage classification strategy combining a one-dimensional Bayesian classifier with a support vector machine. Using this approach we demonstrate that the frequency of a small number of linear motifs three amino acids in length can accurately identify a CD4 T cell response to ovalbumin against a background response to the complex mixture of antigens which characterize Complete Freund's Adjuvant. The sequence data is available at www.ncbi.nlm.nih.gov/sra/?term¼SRP075893 . The Decombinator package is available at github.com/innate2adaptive/Decombinator . The R package e1071 is available at the CRAN repository https://cran.r-project.org/web/packages/e1071/index.html . b.chain@ucl.ac.uk. Supplementary data are available at Bioinformatics online. © The Author 2017. Published by Oxford University Press.

Gradient Evolution-based Support Vector Machine Algorithm for Classification

NASA Astrophysics Data System (ADS)

Zulvia, Ferani E.; Kuo, R. J.

2018-03-01

This paper proposes a classification algorithm based on a support vector machine (SVM) and gradient evolution (GE) algorithms. SVM algorithm has been widely used in classification. However, its result is significantly influenced by the parameters. Therefore, this paper aims to propose an improvement of SVM algorithm which can find the best SVMs’ parameters automatically. The proposed algorithm employs a GE algorithm to automatically determine the SVMs’ parameters. The GE algorithm takes a role as a global optimizer in finding the best parameter which will be used by SVM algorithm. The proposed GE-SVM algorithm is verified using some benchmark datasets and compared with other metaheuristic-based SVM algorithms. The experimental results show that the proposed GE-SVM algorithm obtains better results than other algorithms tested in this paper.

Nonlinear programming for classification problems in machine learning

NASA Astrophysics Data System (ADS)

Astorino, Annabella; Fuduli, Antonio; Gaudioso, Manlio

2016-10-01

We survey some nonlinear models for classification problems arising in machine learning. In the last years this field has become more and more relevant due to a lot of practical applications, such as text and web classification, object recognition in machine vision, gene expression profile analysis, DNA and protein analysis, medical diagnosis, customer profiling etc. Classification deals with separation of sets by means of appropriate separation surfaces, which is generally obtained by solving a numerical optimization model. While linear separability is the basis of the most popular approach to classification, the Support Vector Machine (SVM), in the recent years using nonlinear separating surfaces has received some attention. The objective of this work is to recall some of such proposals, mainly in terms of the numerical optimization models. In particular we tackle the polyhedral, ellipsoidal, spherical and conical separation approaches and, for some of them, we also consider the semisupervised versions.

Prediction of B-cell linear epitopes with a combination of support vector machine classification and amino acid propensity identification.

PubMed

Wang, Hsin-Wei; Lin, Ya-Chi; Pai, Tun-Wen; Chang, Hao-Teng

2011-01-01

Epitopes are antigenic determinants that are useful because they induce B-cell antibody production and stimulate T-cell activation. Bioinformatics can enable rapid, efficient prediction of potential epitopes. Here, we designed a novel B-cell linear epitope prediction system called LEPS, Linear Epitope Prediction by Propensities and Support Vector Machine, that combined physico-chemical propensity identification and support vector machine (SVM) classification. We tested the LEPS on four datasets: AntiJen, HIV, a newly generated PC, and AHP, a combination of these three datasets. Peptides with globally or locally high physicochemical propensities were first identified as primitive linear epitope (LE) candidates. Then, candidates were classified with the SVM based on the unique features of amino acid segments. This reduced the number of predicted epitopes and enhanced the positive prediction value (PPV). Compared to four other well-known LE prediction systems, the LEPS achieved the highest accuracy (72.52%), specificity (84.22%), PPV (32.07%), and Matthews' correlation coefficient (10.36%).

Weighted K-means support vector machine for cancer prediction.

PubMed

Kim, SungHwan

2016-01-01

To date, the support vector machine (SVM) has been widely applied to diverse bio-medical fields to address disease subtype identification and pathogenicity of genetic variants. In this paper, I propose the weighted K-means support vector machine (wKM-SVM) and weighted support vector machine (wSVM), for which I allow the SVM to impose weights to the loss term. Besides, I demonstrate the numerical relations between the objective function of the SVM and weights. Motivated by general ensemble techniques, which are known to improve accuracy, I directly adopt the boosting algorithm to the newly proposed weighted KM-SVM (and wSVM). For predictive performance, a range of simulation studies demonstrate that the weighted KM-SVM (and wSVM) with boosting outperforms the standard KM-SVM (and SVM) including but not limited to many popular classification rules. I applied the proposed methods to simulated data and two large-scale real applications in the TCGA pan-cancer methylation data of breast and kidney cancer. In conclusion, the weighted KM-SVM (and wSVM) increases accuracy of the classification model, and will facilitate disease diagnosis and clinical treatment decisions to benefit patients. A software package (wSVM) is publicly available at the R-project webpage (https://www.r-project.org).

Efficacy of hidden markov model over support vector machine on multiclass classification of healthy and cancerous cervical tissues

NASA Astrophysics Data System (ADS)

Mukhopadhyay, Sabyasachi; Kurmi, Indrajit; Pratiher, Sawon; Mukherjee, Sukanya; Barman, Ritwik; Ghosh, Nirmalya; Panigrahi, Prasanta K.

2018-02-01

In this paper, a comparative study between SVM and HMM has been carried out for multiclass classification of cervical healthy and cancerous tissues. In our study, the HMM methodology is more promising to produce higher accuracy in classification.

Exploring the impact of wavelet-based denoising in the classification of remote sensing hyperspectral images

NASA Astrophysics Data System (ADS)

Quesada-Barriuso, Pablo; Heras, Dora B.; Argüello, Francisco

2016-10-01

The classification of remote sensing hyperspectral images for land cover applications is a very intensive topic. In the case of supervised classification, Support Vector Machines (SVMs) play a dominant role. Recently, the Extreme Learning Machine algorithm (ELM) has been extensively used. The classification scheme previously published by the authors, and called WT-EMP, introduces spatial information in the classification process by means of an Extended Morphological Profile (EMP) that is created from features extracted by wavelets. In addition, the hyperspectral image is denoised in the 2-D spatial domain, also using wavelets and it is joined to the EMP via a stacked vector. In this paper, the scheme is improved achieving two goals. The first one is to reduce the classification time while preserving the accuracy of the classification by using ELM instead of SVM. The second one is to improve the accuracy results by performing not only a 2-D denoising for every spectral band, but also a previous additional 1-D spectral signature denoising applied to each pixel vector of the image. For each denoising the image is transformed by applying a 1-D or 2-D wavelet transform, and then a NeighShrink thresholding is applied. Improvements in terms of classification accuracy are obtained, especially for images with close regions in the classification reference map, because in these cases the accuracy of the classification in the edges between classes is more relevant.

A Directed Acyclic Graph-Large Margin Distribution Machine Model for Music Symbol Classification

PubMed Central

Wen, Cuihong; Zhang, Jing; Rebelo, Ana; Cheng, Fanyong

2016-01-01

Optical Music Recognition (OMR) has received increasing attention in recent years. In this paper, we propose a classifier based on a new method named Directed Acyclic Graph-Large margin Distribution Machine (DAG-LDM). The DAG-LDM is an improvement of the Large margin Distribution Machine (LDM), which is a binary classifier that optimizes the margin distribution by maximizing the margin mean and minimizing the margin variance simultaneously. We modify the LDM to the DAG-LDM to solve the multi-class music symbol classification problem. Tests are conducted on more than 10000 music symbol images, obtained from handwritten and printed images of music scores. The proposed method provides superior classification capability and achieves much higher classification accuracy than the state-of-the-art algorithms such as Support Vector Machines (SVMs) and Neural Networks (NNs). PMID:26985826

A Directed Acyclic Graph-Large Margin Distribution Machine Model for Music Symbol Classification.

PubMed

Wen, Cuihong; Zhang, Jing; Rebelo, Ana; Cheng, Fanyong

2016-01-01

Optical Music Recognition (OMR) has received increasing attention in recent years. In this paper, we propose a classifier based on a new method named Directed Acyclic Graph-Large margin Distribution Machine (DAG-LDM). The DAG-LDM is an improvement of the Large margin Distribution Machine (LDM), which is a binary classifier that optimizes the margin distribution by maximizing the margin mean and minimizing the margin variance simultaneously. We modify the LDM to the DAG-LDM to solve the multi-class music symbol classification problem. Tests are conducted on more than 10000 music symbol images, obtained from handwritten and printed images of music scores. The proposed method provides superior classification capability and achieves much higher classification accuracy than the state-of-the-art algorithms such as Support Vector Machines (SVMs) and Neural Networks (NNs).

Research on intrusion detection based on Kohonen network and support vector machine

NASA Astrophysics Data System (ADS)

Shuai, Chunyan; Yang, Hengcheng; Gong, Zeweiyi

2018-05-01

In view of the problem of low detection accuracy and the long detection time of support vector machine, which directly applied to the network intrusion detection system. Optimization of SVM parameters can greatly improve the detection accuracy, but it can not be applied to high-speed network because of the long detection time. a method based on Kohonen neural network feature selection is proposed to reduce the optimization time of support vector machine parameters. Firstly, this paper is to calculate the weights of the KDD99 network intrusion data by Kohonen network and select feature by weight. Then, after the feature selection is completed, genetic algorithm (GA) and grid search method are used for parameter optimization to find the appropriate parameters and classify them by support vector machines. By comparing experiments, it is concluded that feature selection can reduce the time of parameter optimization, which has little influence on the accuracy of classification. The experiments suggest that the support vector machine can be used in the network intrusion detection system and reduce the missing rate.

Speech sound classification and detection of articulation disorders with support vector machines and wavelets.

PubMed

Georgoulas, George; Georgopoulos, Voula C; Stylios, Chrysostomos D

2006-01-01

This paper proposes a novel integrated methodology to extract features and classify speech sounds with intent to detect the possible existence of a speech articulation disorder in a speaker. Articulation, in effect, is the specific and characteristic way that an individual produces the speech sounds. A methodology to process the speech signal, extract features and finally classify the signal and detect articulation problems in a speaker is presented. The use of support vector machines (SVMs), for the classification of speech sounds and detection of articulation disorders is introduced. The proposed method is implemented on a data set where different sets of features and different schemes of SVMs are tested leading to satisfactory performance.

Classification of the Regional Ionospheric Disturbance Based on Machine Learning Techniques

NASA Astrophysics Data System (ADS)

Terzi, Merve Begum; Arikan, Orhan; Karatay, Secil; Arikan, Feza; Gulyaeva, Tamara

2016-08-01

In this study, Total Electron Content (TEC) estimated from GPS receivers is used to model the regional and local variability that differs from global activity along with solar and geomagnetic indices. For the automated classification of regional disturbances, a classification technique based on a robust machine learning technique that have found wide spread use, Support Vector Machine (SVM) is proposed. Performance of developed classification technique is demonstrated for midlatitude ionosphere over Anatolia using TEC estimates generated from GPS data provided by Turkish National Permanent GPS Network (TNPGN-Active) for solar maximum year of 2011. As a result of implementing developed classification technique to Global Ionospheric Map (GIM) TEC data, which is provided by the NASA Jet Propulsion Laboratory (JPL), it is shown that SVM can be a suitable learning method to detect anomalies in TEC variations.

Sparse kernel methods for high-dimensional survival data.

PubMed

Evers, Ludger; Messow, Claudia-Martina

2008-07-15

Sparse kernel methods like support vector machines (SVM) have been applied with great success to classification and (standard) regression settings. Existing support vector classification and regression techniques however are not suitable for partly censored survival data, which are typically analysed using Cox's proportional hazards model. As the partial likelihood of the proportional hazards model only depends on the covariates through inner products, it can be 'kernelized'. The kernelized proportional hazards model however yields a solution that is dense, i.e. the solution depends on all observations. One of the key features of an SVM is that it yields a sparse solution, depending only on a small fraction of the training data. We propose two methods. One is based on a geometric idea, where-akin to support vector classification-the margin between the failed observation and the observations currently at risk is maximised. The other approach is based on obtaining a sparse model by adding observations one after another akin to the Import Vector Machine (IVM). Data examples studied suggest that both methods can outperform competing approaches. Software is available under the GNU Public License as an R package and can be obtained from the first author's website http://www.maths.bris.ac.uk/~maxle/software.html.

Prediction of Skin Sensitization with a Particle Swarm Optimized Support Vector Machine

PubMed Central

Yuan, Hua; Huang, Jianping; Cao, Chenzhong

2009-01-01

Skin sensitization is the most commonly reported occupational illness, causing much suffering to a wide range of people. Identification and labeling of environmental allergens is urgently required to protect people from skin sensitization. The guinea pig maximization test (GPMT) and murine local lymph node assay (LLNA) are the two most important in vivo models for identification of skin sensitizers. In order to reduce the number of animal tests, quantitative structure-activity relationships (QSARs) are strongly encouraged in the assessment of skin sensitization of chemicals. This paper has investigated the skin sensitization potential of 162 compounds with LLNA results and 92 compounds with GPMT results using a support vector machine. A particle swarm optimization algorithm was implemented for feature selection from a large number of molecular descriptors calculated by Dragon. For the LLNA data set, the classification accuracies are 95.37% and 88.89% for the training and the test sets, respectively. For the GPMT data set, the classification accuracies are 91.80% and 90.32% for the training and the test sets, respectively. The classification performances were greatly improved compared to those reported in the literature, indicating that the support vector machine optimized by particle swarm in this paper is competent for the identification of skin sensitizers. PMID:19742136

Pharmaceutical Raw Material Identification Using Miniature Near-Infrared (MicroNIR) Spectroscopy and Supervised Pattern Recognition Using Support Vector Machine

PubMed Central

Hsiung, Chang; Pederson, Christopher G.; Zou, Peng; Smith, Valton; von Gunten, Marc; O’Brien, Nada A.

2016-01-01

Near-infrared spectroscopy as a rapid and non-destructive analytical technique offers great advantages for pharmaceutical raw material identification (RMID) to fulfill the quality and safety requirements in pharmaceutical industry. In this study, we demonstrated the use of portable miniature near-infrared (MicroNIR) spectrometers for NIR-based pharmaceutical RMID and solved two challenges in this area, model transferability and large-scale classification, with the aid of support vector machine (SVM) modeling. We used a set of 19 pharmaceutical compounds including various active pharmaceutical ingredients (APIs) and excipients and six MicroNIR spectrometers to test model transferability. For the test of large-scale classification, we used another set of 253 pharmaceutical compounds comprised of both chemically and physically different APIs and excipients. We compared SVM with conventional chemometric modeling techniques, including soft independent modeling of class analogy, partial least squares discriminant analysis, linear discriminant analysis, and quadratic discriminant analysis. Support vector machine modeling using a linear kernel, especially when combined with a hierarchical scheme, exhibited excellent performance in both model transferability and large-scale classification. Hence, ultra-compact, portable and robust MicroNIR spectrometers coupled with SVM modeling can make on-site and in situ pharmaceutical RMID for large-volume applications highly achievable. PMID:27029624

a Hyperspectral Image Classification Method Using Isomap and Rvm

NASA Astrophysics Data System (ADS)

Chang, H.; Wang, T.; Fang, H.; Su, Y.

2018-04-01

Classification is one of the most significant applications of hyperspectral image processing and even remote sensing. Though various algorithms have been proposed to implement and improve this application, there are still drawbacks in traditional classification methods. Thus further investigations on some aspects, such as dimension reduction, data mining, and rational use of spatial information, should be developed. In this paper, we used a widely utilized global manifold learning approach, isometric feature mapping (ISOMAP), to address the intrinsic nonlinearities of hyperspectral image for dimension reduction. Considering the impropriety of Euclidean distance in spectral measurement, we applied spectral angle (SA) for substitute when constructed the neighbourhood graph. Then, relevance vector machines (RVM) was introduced to implement classification instead of support vector machines (SVM) for simplicity, generalization and sparsity. Therefore, a probability result could be obtained rather than a less convincing binary result. Moreover, taking into account the spatial information of the hyperspectral image, we employ a spatial vector formed by different classes' ratios around the pixel. At last, we combined the probability results and spatial factors with a criterion to decide the final classification result. To verify the proposed method, we have implemented multiple experiments with standard hyperspectral images compared with some other methods. The results and different evaluation indexes illustrated the effectiveness of our method.

Expected energy-based restricted Boltzmann machine for classification.

PubMed

Elfwing, S; Uchibe, E; Doya, K

2015-04-01

In classification tasks, restricted Boltzmann machines (RBMs) have predominantly been used in the first stage, either as feature extractors or to provide initialization of neural networks. In this study, we propose a discriminative learning approach to provide a self-contained RBM method for classification, inspired by free-energy based function approximation (FE-RBM), originally proposed for reinforcement learning. For classification, the FE-RBM method computes the output for an input vector and a class vector by the negative free energy of an RBM. Learning is achieved by stochastic gradient-descent using a mean-squared error training objective. In an earlier study, we demonstrated that the performance and the robustness of FE-RBM function approximation can be improved by scaling the free energy by a constant that is related to the size of network. In this study, we propose that the learning performance of RBM function approximation can be further improved by computing the output by the negative expected energy (EE-RBM), instead of the negative free energy. To create a deep learning architecture, we stack several RBMs on top of each other. We also connect the class nodes to all hidden layers to try to improve the performance even further. We validate the classification performance of EE-RBM using the MNIST data set and the NORB data set, achieving competitive performance compared with other classifiers such as standard neural networks, deep belief networks, classification RBMs, and support vector machines. The purpose of using the NORB data set is to demonstrate that EE-RBM with binary input nodes can achieve high performance in the continuous input domain. Copyright © 2014 The Authors. Published by Elsevier Ltd.. All rights reserved.

Complex extreme learning machine applications in terahertz pulsed signals feature sets.

PubMed

Yin, X-X; Hadjiloucas, S; Zhang, Y

2014-11-01

This paper presents a novel approach to the automatic classification of very large data sets composed of terahertz pulse transient signals, highlighting their potential use in biochemical, biomedical, pharmaceutical and security applications. Two different types of THz spectra are considered in the classification process. Firstly a binary classification study of poly-A and poly-C ribonucleic acid samples is performed. This is then contrasted with a difficult multi-class classification problem of spectra from six different powder samples that although have fairly indistinguishable features in the optical spectrum, they also possess a few discernable spectral features in the terahertz part of the spectrum. Classification is performed using a complex-valued extreme learning machine algorithm that takes into account features in both the amplitude as well as the phase of the recorded spectra. Classification speed and accuracy are contrasted with that achieved using a support vector machine classifier. The study systematically compares the classifier performance achieved after adopting different Gaussian kernels when separating amplitude and phase signatures. The two signatures are presented as feature vectors for both training and testing purposes. The study confirms the utility of complex-valued extreme learning machine algorithms for classification of the very large data sets generated with current terahertz imaging spectrometers. The classifier can take into consideration heterogeneous layers within an object as would be required within a tomographic setting and is sufficiently robust to detect patterns hidden inside noisy terahertz data sets. The proposed study opens up the opportunity for the establishment of complex-valued extreme learning machine algorithms as new chemometric tools that will assist the wider proliferation of terahertz sensing technology for chemical sensing, quality control, security screening and clinic diagnosis. Furthermore, the proposed algorithm should also be very useful in other applications requiring the classification of very large datasets. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

Impact of corpus domain for sentiment classification: An evaluation study using supervised machine learning techniques

NASA Astrophysics Data System (ADS)

Karsi, Redouane; Zaim, Mounia; El Alami, Jamila

2017-07-01

Thanks to the development of the internet, a large community now has the possibility to communicate and express its opinions and preferences through multiple media such as blogs, forums, social networks and e-commerce sites. Today, it becomes clearer that opinions published on the web are a very valuable source for decision-making, so a rapidly growing field of research called “sentiment analysis” is born to address the problem of automatically determining the polarity (Positive, negative, neutral,…) of textual opinions. People expressing themselves in a particular domain often use specific domain language expressions, thus, building a classifier, which performs well in different domains is a challenging problem. The purpose of this paper is to evaluate the impact of domain for sentiment classification when using machine learning techniques. In our study three popular machine learning techniques: Support Vector Machines (SVM), Naive Bayes and K nearest neighbors(KNN) were applied on datasets collected from different domains. Experimental results show that Support Vector Machines outperforms other classifiers in all domains, since it achieved at least 74.75% accuracy with a standard deviation of 4,08.

Evaluation of a Machine-Learning Classifier for Keratoconus Detection Based on Scheimpflug Tomography.

PubMed

Ruiz Hidalgo, Irene; Rodriguez, Pablo; Rozema, Jos J; Ní Dhubhghaill, Sorcha; Zakaria, Nadia; Tassignon, Marie-José; Koppen, Carina

2016-06-01

To evaluate the performance of a support vector machine algorithm that automatically and objectively identifies corneal patterns based on a combination of 22 parameters obtained from Pentacam measurements and to compare this method with other known keratoconus (KC) classification methods. Pentacam data from 860 eyes were included in the study and divided into 5 groups: 454 KC, 67 forme fruste (FF), 28 astigmatic, 117 after refractive surgery (PR), and 194 normal eyes (N). Twenty-two parameters were used for classification using a support vector machine algorithm developed in Weka, a machine-learning computer software. The cross-validation accuracy for 3 different classification tasks (KC vs. N, FF vs. N and all 5 groups) was calculated and compared with other known classification methods. The accuracy achieved in the KC versus N discrimination task was 98.9%, with 99.1% sensitivity and 98.5% specificity for KC detection. The accuracy in the FF versus N task was 93.1%, with 79.1% sensitivity and 97.9% specificity for the FF discrimination. Finally, for the 5-groups classification, the accuracy was 88.8%, with a weighted average sensitivity of 89.0% and specificity of 95.2%. Despite using the strictest definition for FF KC, the present study obtained comparable or better results than the single-parameter methods and indices reported in the literature. In some cases, direct comparisons with the literature were not possible because of differences in the compositions and definitions of the study groups, especially the FF KC.

Application of machine learning on brain cancer multiclass classification

NASA Astrophysics Data System (ADS)

Panca, V.; Rustam, Z.

2017-07-01

Classification of brain cancer is a problem of multiclass classification. One approach to solve this problem is by first transforming it into several binary problems. The microarray gene expression dataset has the two main characteristics of medical data: extremely many features (genes) and only a few number of samples. The application of machine learning on microarray gene expression dataset mainly consists of two steps: feature selection and classification. In this paper, the features are selected using a method based on support vector machine recursive feature elimination (SVM-RFE) principle which is improved to solve multiclass classification, called multiple multiclass SVM-RFE. Instead of using only the selected features on a single classifier, this method combines the result of multiple classifiers. The features are divided into subsets and SVM-RFE is used on each subset. Then, the selected features on each subset are put on separate classifiers. This method enhances the feature selection ability of each single SVM-RFE. Twin support vector machine (TWSVM) is used as the method of the classifier to reduce computational complexity. While ordinary SVM finds single optimum hyperplane, the main objective Twin SVM is to find two non-parallel optimum hyperplanes. The experiment on the brain cancer microarray gene expression dataset shows this method could classify 71,4% of the overall test data correctly, using 100 and 1000 genes selected from multiple multiclass SVM-RFE feature selection method. Furthermore, the per class results show that this method could classify data of normal and MD class with 100% accuracy.

LANDMARK-BASED SPEECH RECOGNITION: REPORT OF THE 2004 JOHNS HOPKINS SUMMER WORKSHOP.

PubMed

Hasegawa-Johnson, Mark; Baker, James; Borys, Sarah; Chen, Ken; Coogan, Emily; Greenberg, Steven; Juneja, Amit; Kirchhoff, Katrin; Livescu, Karen; Mohan, Srividya; Muller, Jennifer; Sonmez, Kemal; Wang, Tianyu

2005-01-01

Three research prototype speech recognition systems are described, all of which use recently developed methods from artificial intelligence (specifically support vector machines, dynamic Bayesian networks, and maximum entropy classification) in order to implement, in the form of an automatic speech recognizer, current theories of human speech perception and phonology (specifically landmark-based speech perception, nonlinear phonology, and articulatory phonology). All three systems begin with a high-dimensional multiframe acoustic-to-distinctive feature transformation, implemented using support vector machines trained to detect and classify acoustic phonetic landmarks. Distinctive feature probabilities estimated by the support vector machines are then integrated using one of three pronunciation models: a dynamic programming algorithm that assumes canonical pronunciation of each word, a dynamic Bayesian network implementation of articulatory phonology, or a discriminative pronunciation model trained using the methods of maximum entropy classification. Log probability scores computed by these models are then combined, using log-linear combination, with other word scores available in the lattice output of a first-pass recognizer, and the resulting combination score is used to compute a second-pass speech recognition output.

Deep learning of support vector machines with class probability output networks.

PubMed

Kim, Sangwook; Yu, Zhibin; Kil, Rhee Man; Lee, Minho

2015-04-01

Deep learning methods endeavor to learn features automatically at multiple levels and allow systems to learn complex functions mapping from the input space to the output space for the given data. The ability to learn powerful features automatically is increasingly important as the volume of data and range of applications of machine learning methods continues to grow. This paper proposes a new deep architecture that uses support vector machines (SVMs) with class probability output networks (CPONs) to provide better generalization power for pattern classification problems. As a result, deep features are extracted without additional feature engineering steps, using multiple layers of the SVM classifiers with CPONs. The proposed structure closely approaches the ideal Bayes classifier as the number of layers increases. Using a simulation of classification problems, the effectiveness of the proposed method is demonstrated. Copyright © 2014 Elsevier Ltd. All rights reserved.

New fuzzy support vector machine for the class imbalance problem in medical datasets classification.

PubMed

Gu, Xiaoqing; Ni, Tongguang; Wang, Hongyuan

2014-01-01

In medical datasets classification, support vector machine (SVM) is considered to be one of the most successful methods. However, most of the real-world medical datasets usually contain some outliers/noise and data often have class imbalance problems. In this paper, a fuzzy support machine (FSVM) for the class imbalance problem (called FSVM-CIP) is presented, which can be seen as a modified class of FSVM by extending manifold regularization and assigning two misclassification costs for two classes. The proposed FSVM-CIP can be used to handle the class imbalance problem in the presence of outliers/noise, and enhance the locality maximum margin. Five real-world medical datasets, breast, heart, hepatitis, BUPA liver, and pima diabetes, from the UCI medical database are employed to illustrate the method presented in this paper. Experimental results on these datasets show the outperformed or comparable effectiveness of FSVM-CIP.

Support vector machines-based fault diagnosis for turbo-pump rotor

NASA Astrophysics Data System (ADS)

Yuan, Sheng-Fa; Chu, Fu-Lei

2006-05-01

Most artificial intelligence methods used in fault diagnosis are based on empirical risk minimisation principle and have poor generalisation when fault samples are few. Support vector machines (SVM) is a new general machine-learning tool based on structural risk minimisation principle that exhibits good generalisation even when fault samples are few. Fault diagnosis based on SVM is discussed. Since basic SVM is originally designed for two-class classification, while most of fault diagnosis problems are multi-class cases, a new multi-class classification of SVM named 'one to others' algorithm is presented to solve the multi-class recognition problems. It is a binary tree classifier composed of several two-class classifiers organised by fault priority, which is simple, and has little repeated training amount, and the rate of training and recognition is expedited. The effectiveness of the method is verified by the application to the fault diagnosis for turbo pump rotor.

Distance Metric Learning via Iterated Support Vector Machines.

PubMed

Zuo, Wangmeng; Wang, Faqiang; Zhang, David; Lin, Liang; Huang, Yuchi; Meng, Deyu; Zhang, Lei

2017-07-11

Distance metric learning aims to learn from the given training data a valid distance metric, with which the similarity between data samples can be more effectively evaluated for classification. Metric learning is often formulated as a convex or nonconvex optimization problem, while most existing methods are based on customized optimizers and become inefficient for large scale problems. In this paper, we formulate metric learning as a kernel classification problem with the positive semi-definite constraint, and solve it by iterated training of support vector machines (SVMs). The new formulation is easy to implement and efficient in training with the off-the-shelf SVM solvers. Two novel metric learning models, namely Positive-semidefinite Constrained Metric Learning (PCML) and Nonnegative-coefficient Constrained Metric Learning (NCML), are developed. Both PCML and NCML can guarantee the global optimality of their solutions. Experiments are conducted on general classification, face verification and person re-identification to evaluate our methods. Compared with the state-of-the-art approaches, our methods can achieve comparable classification accuracy and are efficient in training.

Application of support vector machine for the separation of mineralised zones in the Takht-e-Gonbad porphyry deposit, SE Iran

NASA Astrophysics Data System (ADS)

Mahvash Mohammadi, Neda; Hezarkhani, Ardeshir

2018-07-01

Classification of mineralised zones is an important factor for the analysis of economic deposits. In this paper, the support vector machine (SVM), a supervised learning algorithm, based on subsurface data is proposed for classification of mineralised zones in the Takht-e-Gonbad porphyry Cu-deposit (SE Iran). The effects of the input features are evaluated via calculating the accuracy rates on the SVM performance. Ultimately, the SVM model, is developed based on input features namely lithology, alteration, mineralisation, the level and, radial basis function (RBF) as a kernel function. Moreover, the optimal amount of parameters λ and C, using n-fold cross-validation method, are calculated at level 0.001 and 0.01 respectively. The accuracy of this model is 0.931 for classification of mineralised zones in the Takht-e-Gonbad porphyry deposit. The results of the study confirm the efficiency of SVM method for classification the mineralised zones.

A discrete wavelet based feature extraction and hybrid classification technique for microarray data analysis.

PubMed

Bennet, Jaison; Ganaprakasam, Chilambuchelvan Arul; Arputharaj, Kannan

2014-01-01

Cancer classification by doctors and radiologists was based on morphological and clinical features and had limited diagnostic ability in olden days. The recent arrival of DNA microarray technology has led to the concurrent monitoring of thousands of gene expressions in a single chip which stimulates the progress in cancer classification. In this paper, we have proposed a hybrid approach for microarray data classification based on nearest neighbor (KNN), naive Bayes, and support vector machine (SVM). Feature selection prior to classification plays a vital role and a feature selection technique which combines discrete wavelet transform (DWT) and moving window technique (MWT) is used. The performance of the proposed method is compared with the conventional classifiers like support vector machine, nearest neighbor, and naive Bayes. Experiments have been conducted on both real and benchmark datasets and the results indicate that the ensemble approach produces higher classification accuracy than conventional classifiers. This paper serves as an automated system for the classification of cancer and can be applied by doctors in real cases which serve as a boon to the medical community. This work further reduces the misclassification of cancers which is highly not allowed in cancer detection.

Support vector machine for automatic pain recognition

NASA Astrophysics Data System (ADS)

Monwar, Md Maruf; Rezaei, Siamak

2009-02-01

Facial expressions are a key index of emotion and the interpretation of such expressions of emotion is critical to everyday social functioning. In this paper, we present an efficient video analysis technique for recognition of a specific expression, pain, from human faces. We employ an automatic face detector which detects face from the stored video frame using skin color modeling technique. For pain recognition, location and shape features of the detected faces are computed. These features are then used as inputs to a support vector machine (SVM) for classification. We compare the results with neural network based and eigenimage based automatic pain recognition systems. The experiment results indicate that using support vector machine as classifier can certainly improve the performance of automatic pain recognition system.

Cervical cancer survival prediction using hybrid of SMOTE, CART and smooth support vector machine

NASA Astrophysics Data System (ADS)

Purnami, S. W.; Khasanah, P. M.; Sumartini, S. H.; Chosuvivatwong, V.; Sriplung, H.

2016-04-01

According to the WHO, every two minutes there is one patient who died from cervical cancer. The high mortality rate is due to the lack of awareness of women for early detection. There are several factors that supposedly influence the survival of cervical cancer patients, including age, anemia status, stage, type of treatment, complications and secondary disease. This study wants to classify/predict cervical cancer survival based on those factors. Various classifications methods: classification and regression tree (CART), smooth support vector machine (SSVM), three order spline SSVM (TSSVM) were used. Since the data of cervical cancer are imbalanced, synthetic minority oversampling technique (SMOTE) is used for handling imbalanced dataset. Performances of these methods are evaluated using accuracy, sensitivity and specificity. Results of this study show that balancing data using SMOTE as preprocessing can improve performance of classification. The SMOTE-SSVM method provided better result than SMOTE-TSSVM and SMOTE-CART.

Application of support vector machines for copper potential mapping in Kerman region, Iran

NASA Astrophysics Data System (ADS)

Shabankareh, Mahdi; Hezarkhani, Ardeshir

2017-04-01

The first step in systematic exploration studies is mineral potential mapping, which involves classification of the study area to favorable and unfavorable parts. Support vector machines (SVM) are designed for supervised classification based on statistical learning theory. This method named support vector classification (SVC). This paper describes SVC model, which combine exploration data in the regional-scale for copper potential mapping in Kerman copper bearing belt in south of Iran. Data layers or evidential maps were in six datasets namely lithology, tectonic, airborne geophysics, ferric alteration, hydroxide alteration and geochemistry. The SVC modeling result selected 2220 pixels as favorable zones, approximately 25 percent of the study area. Besides, 66 out of 86 copper indices, approximately 78.6% of all, were located in favorable zones. Other main goal of this study was to determine how each input affects favorable output. For this purpose, the histogram of each normalized input data to its favorable output was drawn. The histograms of each input dataset for favorable output showed that each information layer had a certain pattern. These patterns of SVC results could be considered as regional copper exploration characteristics.

Supervised machine learning algorithms to diagnose stress for vehicle drivers based on physiological sensor signals.

PubMed

Barua, Shaibal; Begum, Shahina; Ahmed, Mobyen Uddin

2015-01-01

Machine learning algorithms play an important role in computer science research. Recent advancement in sensor data collection in clinical sciences lead to a complex, heterogeneous data processing, and analysis for patient diagnosis and prognosis. Diagnosis and treatment of patients based on manual analysis of these sensor data are difficult and time consuming. Therefore, development of Knowledge-based systems to support clinicians in decision-making is important. However, it is necessary to perform experimental work to compare performances of different machine learning methods to help to select appropriate method for a specific characteristic of data sets. This paper compares classification performance of three popular machine learning methods i.e., case-based reasoning, neutral networks and support vector machine to diagnose stress of vehicle drivers using finger temperature and heart rate variability. The experimental results show that case-based reasoning outperforms other two methods in terms of classification accuracy. Case-based reasoning has achieved 80% and 86% accuracy to classify stress using finger temperature and heart rate variability. On contrary, both neural network and support vector machine have achieved less than 80% accuracy by using both physiological signals.

Analysis of miRNA expression profile based on SVM algorithm

NASA Astrophysics Data System (ADS)

Ting-ting, Dai; Chang-ji, Shan; Yan-shou, Dong; Yi-duo, Bian

2018-05-01

Based on mirna expression spectrum data set, a new data mining algorithm - tSVM - KNN (t statistic with support vector machine - k nearest neighbor) is proposed. the idea of the algorithm is: firstly, the feature selection of the data set is carried out by the unified measurement method; Secondly, SVM - KNN algorithm, which combines support vector machine (SVM) and k - nearest neighbor (k - nearest neighbor) is used as classifier. Simulation results show that SVM - KNN algorithm has better classification ability than SVM and KNN alone. Tsvm - KNN algorithm only needs 5 mirnas to obtain 96.08 % classification accuracy in terms of the number of mirna " tags" and recognition accuracy. compared with similar algorithms, tsvm - KNN algorithm has obvious advantages.

Automatic EEG artifact removal: a weighted support vector machine approach with error correction.

PubMed

Shao, Shi-Yun; Shen, Kai-Quan; Ong, Chong Jin; Wilder-Smith, Einar P V; Li, Xiao-Ping

2009-02-01

An automatic electroencephalogram (EEG) artifact removal method is presented in this paper. Compared to past methods, it has two unique features: 1) a weighted version of support vector machine formulation that handles the inherent unbalanced nature of component classification and 2) the ability to accommodate structural information typically found in component classification. The advantages of the proposed method are demonstrated on real-life EEG recordings with comparisons made to several benchmark methods. Results show that the proposed method is preferable to the other methods in the context of artifact removal by achieving a better tradeoff between removing artifacts and preserving inherent brain activities. Qualitative evaluation of the reconstructed EEG epochs also demonstrates that after artifact removal inherent brain activities are largely preserved.

Facial Expression Recognition using Multiclass Ensemble Least-Square Support Vector Machine

NASA Astrophysics Data System (ADS)

Lawi, Armin; Sya'Rani Machrizzandi, M.

2018-03-01

Facial expression is one of behavior characteristics of human-being. The use of biometrics technology system with facial expression characteristics makes it possible to recognize a person’s mood or emotion. The basic components of facial expression analysis system are face detection, face image extraction, facial classification and facial expressions recognition. This paper uses Principal Component Analysis (PCA) algorithm to extract facial features with expression parameters, i.e., happy, sad, neutral, angry, fear, and disgusted. Then Multiclass Ensemble Least-Squares Support Vector Machine (MELS-SVM) is used for the classification process of facial expression. The result of MELS-SVM model obtained from our 185 different expression images of 10 persons showed high accuracy level of 99.998% using RBF kernel.

ATLS Hypovolemic Shock Classification by Prediction of Blood Loss in Rats Using Regression Models.

PubMed

Choi, Soo Beom; Choi, Joon Yul; Park, Jee Soo; Kim, Deok Won

2016-07-01

In our previous study, our input data set consisted of 78 rats, the blood loss in percent as a dependent variable, and 11 independent variables (heart rate, systolic blood pressure, diastolic blood pressure, mean arterial pressure, pulse pressure, respiration rate, temperature, perfusion index, lactate concentration, shock index, and new index (lactate concentration/perfusion)). The machine learning methods for multicategory classification were applied to a rat model in acute hemorrhage to predict the four Advanced Trauma Life Support (ATLS) hypovolemic shock classes for triage in our previous study. However, multicategory classification is much more difficult and complicated than binary classification. We introduce a simple approach for classifying ATLS hypovolaemic shock class by predicting blood loss in percent using support vector regression and multivariate linear regression (MLR). We also compared the performance of the classification models using absolute and relative vital signs. The accuracies of support vector regression and MLR models with relative values by predicting blood loss in percent were 88.5% and 84.6%, respectively. These were better than the best accuracy of 80.8% of the direct multicategory classification using the support vector machine one-versus-one model in our previous study for the same validation data set. Moreover, the simple MLR models with both absolute and relative values could provide possibility of the future clinical decision support system for ATLS classification. The perfusion index and new index were more appropriate with relative changes than absolute values.

Assessment of various supervised learning algorithms using different performance metrics

NASA Astrophysics Data System (ADS)

Susheel Kumar, S. M.; Laxkar, Deepak; Adhikari, Sourav; Vijayarajan, V.

2017-11-01

Our work brings out comparison based on the performance of supervised machine learning algorithms on a binary classification task. The supervised machine learning algorithms which are taken into consideration in the following work are namely Support Vector Machine(SVM), Decision Tree(DT), K Nearest Neighbour (KNN), Naïve Bayes(NB) and Random Forest(RF). This paper mostly focuses on comparing the performance of above mentioned algorithms on one binary classification task by analysing the Metrics such as Accuracy, F-Measure, G-Measure, Precision, Misclassification Rate, False Positive Rate, True Positive Rate, Specificity, Prevalence.

Feature selection using a one dimensional naïve Bayes’ classifier increases the accuracy of support vector machine classification of CDR3 repertoires

PubMed Central

Cinelli, Mattia; Sun, , Yuxin; Best, Katharine; Heather, James M.; Reich-Zeliger, Shlomit; Shifrut, Eric; Friedman, Nir; Shawe-Taylor, John; Chain, Benny

2017-01-01

Abstract Motivation: Somatic DNA recombination, the hallmark of vertebrate adaptive immunity, has the potential to generate a vast diversity of antigen receptor sequences. How this diversity captures antigen specificity remains incompletely understood. In this study we use high throughput sequencing to compare the global changes in T cell receptor β chain complementarity determining region 3 (CDR3β) sequences following immunization with ovalbumin administered with complete Freund’s adjuvant (CFA) or CFA alone. Results: The CDR3β sequences were deconstructed into short stretches of overlapping contiguous amino acids. The motifs were ranked according to a one-dimensional Bayesian classifier score comparing their frequency in the repertoires of the two immunization classes. The top ranking motifs were selected and used to create feature vectors which were used to train a support vector machine. The support vector machine achieved high classification scores in a leave-one-out validation test reaching >90% in some cases. Summary: The study describes a novel two-stage classification strategy combining a one-dimensional Bayesian classifier with a support vector machine. Using this approach we demonstrate that the frequency of a small number of linear motifs three amino acids in length can accurately identify a CD4 T cell response to ovalbumin against a background response to the complex mixture of antigens which characterize Complete Freund’s Adjuvant. Availability and implementation: The sequence data is available at www.ncbi.nlm.nih.gov/sra/?term¼SRP075893. The Decombinator package is available at github.com/innate2adaptive/Decombinator. The R package e1071 is available at the CRAN repository https://cran.r-project.org/web/packages/e1071/index.html. Contact: b.chain@ucl.ac.uk Supplementary information: Supplementary data are available at Bioinformatics online. PMID:28073756

Voice based gender classification using machine learning

NASA Astrophysics Data System (ADS)

Raahul, A.; Sapthagiri, R.; Pankaj, K.; Vijayarajan, V.

2017-11-01

Gender identification is one of the major problem speech analysis today. Tracing the gender from acoustic data i.e., pitch, median, frequency etc. Machine learning gives promising results for classification problem in all the research domains. There are several performance metrics to evaluate algorithms of an area. Our Comparative model algorithm for evaluating 5 different machine learning algorithms based on eight different metrics in gender classification from acoustic data. Agenda is to identify gender, with five different algorithms: Linear Discriminant Analysis (LDA), K-Nearest Neighbour (KNN), Classification and Regression Trees (CART), Random Forest (RF), and Support Vector Machine (SVM) on basis of eight different metrics. The main parameter in evaluating any algorithms is its performance. Misclassification rate must be less in classification problems, which says that the accuracy rate must be high. Location and gender of the person have become very crucial in economic markets in the form of AdSense. Here with this comparative model algorithm, we are trying to assess the different ML algorithms and find the best fit for gender classification of acoustic data.

Online Sequential Projection Vector Machine with Adaptive Data Mean Update

PubMed Central

Chen, Lin; Jia, Ji-Ting; Zhang, Qiong; Deng, Wan-Yu; Wei, Wei

2016-01-01

We propose a simple online learning algorithm especial for high-dimensional data. The algorithm is referred to as online sequential projection vector machine (OSPVM) which derives from projection vector machine and can learn from data in one-by-one or chunk-by-chunk mode. In OSPVM, data centering, dimension reduction, and neural network training are integrated seamlessly. In particular, the model parameters including (1) the projection vectors for dimension reduction, (2) the input weights, biases, and output weights, and (3) the number of hidden nodes can be updated simultaneously. Moreover, only one parameter, the number of hidden nodes, needs to be determined manually, and this makes it easy for use in real applications. Performance comparison was made on various high-dimensional classification problems for OSPVM against other fast online algorithms including budgeted stochastic gradient descent (BSGD) approach, adaptive multihyperplane machine (AMM), primal estimated subgradient solver (Pegasos), online sequential extreme learning machine (OSELM), and SVD + OSELM (feature selection based on SVD is performed before OSELM). The results obtained demonstrated the superior generalization performance and efficiency of the OSPVM. PMID:27143958

Online Sequential Projection Vector Machine with Adaptive Data Mean Update.

PubMed

Chen, Lin; Jia, Ji-Ting; Zhang, Qiong; Deng, Wan-Yu; Wei, Wei

2016-01-01

We propose a simple online learning algorithm especial for high-dimensional data. The algorithm is referred to as online sequential projection vector machine (OSPVM) which derives from projection vector machine and can learn from data in one-by-one or chunk-by-chunk mode. In OSPVM, data centering, dimension reduction, and neural network training are integrated seamlessly. In particular, the model parameters including (1) the projection vectors for dimension reduction, (2) the input weights, biases, and output weights, and (3) the number of hidden nodes can be updated simultaneously. Moreover, only one parameter, the number of hidden nodes, needs to be determined manually, and this makes it easy for use in real applications. Performance comparison was made on various high-dimensional classification problems for OSPVM against other fast online algorithms including budgeted stochastic gradient descent (BSGD) approach, adaptive multihyperplane machine (AMM), primal estimated subgradient solver (Pegasos), online sequential extreme learning machine (OSELM), and SVD + OSELM (feature selection based on SVD is performed before OSELM). The results obtained demonstrated the superior generalization performance and efficiency of the OSPVM.

A Model-Free Machine Learning Method for Risk Classification and Survival Probability Prediction.

PubMed

Geng, Yuan; Lu, Wenbin; Zhang, Hao Helen

2014-01-01

Risk classification and survival probability prediction are two major goals in survival data analysis since they play an important role in patients' risk stratification, long-term diagnosis, and treatment selection. In this article, we propose a new model-free machine learning framework for risk classification and survival probability prediction based on weighted support vector machines. The new procedure does not require any specific parametric or semiparametric model assumption on data, and is therefore capable of capturing nonlinear covariate effects. We use numerous simulation examples to demonstrate finite sample performance of the proposed method under various settings. Applications to a glioma tumor data and a breast cancer gene expression survival data are shown to illustrate the new methodology in real data analysis.

Classification of large-sized hyperspectral imagery using fast machine learning algorithms

NASA Astrophysics Data System (ADS)

Xia, Junshi; Yokoya, Naoto; Iwasaki, Akira

2017-07-01

We present a framework of fast machine learning algorithms in the context of large-sized hyperspectral images classification from the theoretical to a practical viewpoint. In particular, we assess the performance of random forest (RF), rotation forest (RoF), and extreme learning machine (ELM) and the ensembles of RF and ELM. These classifiers are applied to two large-sized hyperspectral images and compared to the support vector machines. To give the quantitative analysis, we pay attention to comparing these methods when working with high input dimensions and a limited/sufficient training set. Moreover, other important issues such as the computational cost and robustness against the noise are also discussed.

A Novel Extreme Learning Machine Classification Model for e-Nose Application Based on the Multiple Kernel Approach.

PubMed

Jian, Yulin; Huang, Daoyu; Yan, Jia; Lu, Kun; Huang, Ying; Wen, Tailai; Zeng, Tanyue; Zhong, Shijie; Xie, Qilong

2017-06-19

A novel classification model, named the quantum-behaved particle swarm optimization (QPSO)-based weighted multiple kernel extreme learning machine (QWMK-ELM), is proposed in this paper. Experimental validation is carried out with two different electronic nose (e-nose) datasets. Being different from the existing multiple kernel extreme learning machine (MK-ELM) algorithms, the combination coefficients of base kernels are regarded as external parameters of single-hidden layer feedforward neural networks (SLFNs). The combination coefficients of base kernels, the model parameters of each base kernel, and the regularization parameter are optimized by QPSO simultaneously before implementing the kernel extreme learning machine (KELM) with the composite kernel function. Four types of common single kernel functions (Gaussian kernel, polynomial kernel, sigmoid kernel, and wavelet kernel) are utilized to constitute different composite kernel functions. Moreover, the method is also compared with other existing classification methods: extreme learning machine (ELM), kernel extreme learning machine (KELM), k-nearest neighbors (KNN), support vector machine (SVM), multi-layer perceptron (MLP), radical basis function neural network (RBFNN), and probabilistic neural network (PNN). The results have demonstrated that the proposed QWMK-ELM outperforms the aforementioned methods, not only in precision, but also in efficiency for gas classification.

Ensemble support vector machine classification of dementia using structural MRI and mini-mental state examination.

PubMed

Sørensen, Lauge; Nielsen, Mads

2018-05-15

The International Challenge for Automated Prediction of MCI from MRI data offered independent, standardized comparison of machine learning algorithms for multi-class classification of normal control (NC), mild cognitive impairment (MCI), converting MCI (cMCI), and Alzheimer's disease (AD) using brain imaging and general cognition. We proposed to use an ensemble of support vector machines (SVMs) that combined bagging without replacement and feature selection. SVM is the most commonly used algorithm in multivariate classification of dementia, and it was therefore valuable to evaluate the potential benefit of ensembling this type of classifier. The ensemble SVM, using either a linear or a radial basis function (RBF) kernel, achieved multi-class classification accuracies of 55.6% and 55.0% in the challenge test set (60 NC, 60 MCI, 60 cMCI, 60 AD), resulting in a third place in the challenge. Similar feature subset sizes were obtained for both kernels, and the most frequently selected MRI features were the volumes of the two hippocampal subregions left presubiculum and right subiculum. Post-challenge analysis revealed that enforcing a minimum number of selected features and increasing the number of ensemble classifiers improved classification accuracy up to 59.1%. The ensemble SVM outperformed single SVM classifications consistently in the challenge test set. Ensemble methods using bagging and feature selection can improve the performance of the commonly applied SVM classifier in dementia classification. This resulted in competitive classification accuracies in the International Challenge for Automated Prediction of MCI from MRI data. Copyright © 2018 Elsevier B.V. All rights reserved.

Classification of diesel pool refinery streams through near infrared spectroscopy and support vector machines using C-SVC and ν-SVC.

PubMed

Alves, Julio Cesar L; Henriques, Claudete B; Poppi, Ronei J

2014-01-03

The use of near infrared (NIR) spectroscopy combined with chemometric methods have been widely used in petroleum and petrochemical industry and provides suitable methods for process control and quality control. The algorithm support vector machines (SVM) has demonstrated to be a powerful chemometric tool for development of classification models due to its ability to nonlinear modeling and with high generalization capability and these characteristics can be especially important for treating near infrared (NIR) spectroscopy data of complex mixtures such as petroleum refinery streams. In this work, a study on the performance of the support vector machines algorithm for classification was carried out, using C-SVC and ν-SVC, applied to near infrared (NIR) spectroscopy data of different types of streams that make up the diesel pool in a petroleum refinery: light gas oil, heavy gas oil, hydrotreated diesel, kerosene, heavy naphtha and external diesel. In addition to these six streams, the diesel final blend produced in the refinery was added to complete the data set. C-SVC and ν-SVC classification models with 2, 4, 6 and 7 classes were developed for comparison between its results and also for comparison with the soft independent modeling of class analogy (SIMCA) models results. It is demonstrated the superior performance of SVC models especially using ν-SVC for development of classification models for 6 and 7 classes leading to an improvement of sensitivity on validation sample sets of 24% and 15%, respectively, when compared to SIMCA models, providing better identification of chemical compositions of different diesel pool refinery streams. Copyright © 2013 Elsevier B.V. All rights reserved.

Wearable-Sensor-Based Classification Models of Faller Status in Older Adults.

PubMed

Howcroft, Jennifer; Lemaire, Edward D; Kofman, Jonathan

2016-01-01

Wearable sensors have potential for quantitative, gait-based, point-of-care fall risk assessment that can be easily and quickly implemented in clinical-care and older-adult living environments. This investigation generated models for wearable-sensor based fall-risk classification in older adults and identified the optimal sensor type, location, combination, and modelling method; for walking with and without a cognitive load task. A convenience sample of 100 older individuals (75.5 ± 6.7 years; 76 non-fallers, 24 fallers based on 6 month retrospective fall occurrence) walked 7.62 m under single-task and dual-task conditions while wearing pressure-sensing insoles and tri-axial accelerometers at the head, pelvis, and left and right shanks. Participants also completed the Activities-specific Balance Confidence scale, Community Health Activities Model Program for Seniors questionnaire, six minute walk test, and ranked their fear of falling. Fall risk classification models were assessed for all sensor combinations and three model types: multi-layer perceptron neural network, naïve Bayesian, and support vector machine. The best performing model was a multi-layer perceptron neural network with input parameters from pressure-sensing insoles and head, pelvis, and left shank accelerometers (accuracy = 84%, F1 score = 0.600, MCC score = 0.521). Head sensor-based models had the best performance of the single-sensor models for single-task gait assessment. Single-task gait assessment models outperformed models based on dual-task walking or clinical assessment data. Support vector machines and neural networks were the best modelling technique for fall risk classification. Fall risk classification models developed for point-of-care environments should be developed using support vector machines and neural networks, with a multi-sensor single-task gait assessment.

Classification of different kinds of pesticide residues on lettuce based on fluorescence spectra and WT-BCC-SVM algorithm

NASA Astrophysics Data System (ADS)

Zhou, Xin; Jun, Sun; Zhang, Bing; Jun, Wu

2017-07-01

In order to improve the reliability of the spectrum feature extracted by wavelet transform, a method combining wavelet transform (WT) with bacterial colony chemotaxis algorithm and support vector machine (BCC-SVM) algorithm (WT-BCC-SVM) was proposed in this paper. Besides, we aimed to identify different kinds of pesticide residues on lettuce leaves in a novel and rapid non-destructive way by using fluorescence spectra technology. The fluorescence spectral data of 150 lettuce leaf samples of five different kinds of pesticide residues on the surface of lettuce were obtained using Cary Eclipse fluorescence spectrometer. Standard normalized variable detrending (SNV detrending), Savitzky-Golay coupled with Standard normalized variable detrending (SG-SNV detrending) were used to preprocess the raw spectra, respectively. Bacterial colony chemotaxis combined with support vector machine (BCC-SVM) and support vector machine (SVM) classification models were established based on full spectra (FS) and wavelet transform characteristics (WTC), respectively. Moreover, WTC were selected by WT. The results showed that the accuracy of training set, calibration set and the prediction set of the best optimal classification model (SG-SNV detrending-WT-BCC-SVM) were 100%, 98% and 93.33%, respectively. In addition, the results indicated that it was feasible to use WT-BCC-SVM to establish diagnostic model of different kinds of pesticide residues on lettuce leaves.

TWSVR: Regression via Twin Support Vector Machine.

PubMed

Khemchandani, Reshma; Goyal, Keshav; Chandra, Suresh

2016-02-01

Taking motivation from Twin Support Vector Machine (TWSVM) formulation, Peng (2010) attempted to propose Twin Support Vector Regression (TSVR) where the regressor is obtained via solving a pair of quadratic programming problems (QPPs). In this paper we argue that TSVR formulation is not in the true spirit of TWSVM. Further, taking motivation from Bi and Bennett (2003), we propose an alternative approach to find a formulation for Twin Support Vector Regression (TWSVR) which is in the true spirit of TWSVM. We show that our proposed TWSVR can be derived from TWSVM for an appropriately constructed classification problem. To check the efficacy of our proposed TWSVR we compare its performance with TSVR and classical Support Vector Regression(SVR) on various regression datasets. Copyright © 2015 Elsevier Ltd. All rights reserved.

A multi-label learning based kernel automatic recommendation method for support vector machine.

PubMed

Zhang, Xueying; Song, Qinbao

2015-01-01

Choosing an appropriate kernel is very important and critical when classifying a new problem with Support Vector Machine. So far, more attention has been paid on constructing new kernels and choosing suitable parameter values for a specific kernel function, but less on kernel selection. Furthermore, most of current kernel selection methods focus on seeking a best kernel with the highest classification accuracy via cross-validation, they are time consuming and ignore the differences among the number of support vectors and the CPU time of SVM with different kernels. Considering the tradeoff between classification success ratio and CPU time, there may be multiple kernel functions performing equally well on the same classification problem. Aiming to automatically select those appropriate kernel functions for a given data set, we propose a multi-label learning based kernel recommendation method built on the data characteristics. For each data set, the meta-knowledge data base is first created by extracting the feature vector of data characteristics and identifying the corresponding applicable kernel set. Then the kernel recommendation model is constructed on the generated meta-knowledge data base with the multi-label classification method. Finally, the appropriate kernel functions are recommended to a new data set by the recommendation model according to the characteristics of the new data set. Extensive experiments over 132 UCI benchmark data sets, with five different types of data set characteristics, eleven typical kernels (Linear, Polynomial, Radial Basis Function, Sigmoidal function, Laplace, Multiquadric, Rational Quadratic, Spherical, Spline, Wave and Circular), and five multi-label classification methods demonstrate that, compared with the existing kernel selection methods and the most widely used RBF kernel function, SVM with the kernel function recommended by our proposed method achieved the highest classification performance.

A Multi-Label Learning Based Kernel Automatic Recommendation Method for Support Vector Machine

PubMed Central

Zhang, Xueying; Song, Qinbao

2015-01-01

Choosing an appropriate kernel is very important and critical when classifying a new problem with Support Vector Machine. So far, more attention has been paid on constructing new kernels and choosing suitable parameter values for a specific kernel function, but less on kernel selection. Furthermore, most of current kernel selection methods focus on seeking a best kernel with the highest classification accuracy via cross-validation, they are time consuming and ignore the differences among the number of support vectors and the CPU time of SVM with different kernels. Considering the tradeoff between classification success ratio and CPU time, there may be multiple kernel functions performing equally well on the same classification problem. Aiming to automatically select those appropriate kernel functions for a given data set, we propose a multi-label learning based kernel recommendation method built on the data characteristics. For each data set, the meta-knowledge data base is first created by extracting the feature vector of data characteristics and identifying the corresponding applicable kernel set. Then the kernel recommendation model is constructed on the generated meta-knowledge data base with the multi-label classification method. Finally, the appropriate kernel functions are recommended to a new data set by the recommendation model according to the characteristics of the new data set. Extensive experiments over 132 UCI benchmark data sets, with five different types of data set characteristics, eleven typical kernels (Linear, Polynomial, Radial Basis Function, Sigmoidal function, Laplace, Multiquadric, Rational Quadratic, Spherical, Spline, Wave and Circular), and five multi-label classification methods demonstrate that, compared with the existing kernel selection methods and the most widely used RBF kernel function, SVM with the kernel function recommended by our proposed method achieved the highest classification performance. PMID:25893896

Optimizing Support Vector Machine Parameters with Genetic Algorithm for Credit Risk Assessment

NASA Astrophysics Data System (ADS)

Manurung, Jonson; Mawengkang, Herman; Zamzami, Elviawaty

2017-12-01

Support vector machine (SVM) is a popular classification method known to have strong generalization capabilities. SVM can solve the problem of classification and linear regression or nonlinear kernel which can be a learning algorithm for the ability of classification and regression. However, SVM also has a weakness that is difficult to determine the optimal parameter value. SVM calculates the best linear separator on the input feature space according to the training data. To classify data which are non-linearly separable, SVM uses kernel tricks to transform the data into a linearly separable data on a higher dimension feature space. The kernel trick using various kinds of kernel functions, such as : linear kernel, polynomial, radial base function (RBF) and sigmoid. Each function has parameters which affect the accuracy of SVM classification. To solve the problem genetic algorithms are proposed to be applied as the optimal parameter value search algorithm thus increasing the best classification accuracy on SVM. Data taken from UCI repository of machine learning database: Australian Credit Approval. The results show that the combination of SVM and genetic algorithms is effective in improving classification accuracy. Genetic algorithms has been shown to be effective in systematically finding optimal kernel parameters for SVM, instead of randomly selected kernel parameters. The best accuracy for data has been upgraded from kernel Linear: 85.12%, polynomial: 81.76%, RBF: 77.22% Sigmoid: 78.70%. However, for bigger data sizes, this method is not practical because it takes a lot of time.

Entanglement-Based Machine Learning on a Quantum Computer

NASA Astrophysics Data System (ADS)

Cai, X.-D.; Wu, D.; Su, Z.-E.; Chen, M.-C.; Wang, X.-L.; Li, Li; Liu, N.-L.; Lu, C.-Y.; Pan, J.-W.

2015-03-01

Machine learning, a branch of artificial intelligence, learns from previous experience to optimize performance, which is ubiquitous in various fields such as computer sciences, financial analysis, robotics, and bioinformatics. A challenge is that machine learning with the rapidly growing "big data" could become intractable for classical computers. Recently, quantum machine learning algorithms [Lloyd, Mohseni, and Rebentrost, arXiv.1307.0411] were proposed which could offer an exponential speedup over classical algorithms. Here, we report the first experimental entanglement-based classification of two-, four-, and eight-dimensional vectors to different clusters using a small-scale photonic quantum computer, which are then used to implement supervised and unsupervised machine learning. The results demonstrate the working principle of using quantum computers to manipulate and classify high-dimensional vectors, the core mathematical routine in machine learning. The method can, in principle, be scaled to larger numbers of qubits, and may provide a new route to accelerate machine learning.

Perspectives on Machine Learning for Classification of Schizotypy Using fMRI Data.

PubMed

Madsen, Kristoffer H; Krohne, Laerke G; Cai, Xin-Lu; Wang, Yi; Chan, Raymond C K

2018-03-15

Functional magnetic resonance imaging is capable of estimating functional activation and connectivity in the human brain, and lately there has been increased interest in the use of these functional modalities combined with machine learning for identification of psychiatric traits. While these methods bear great potential for early diagnosis and better understanding of disease processes, there are wide ranges of processing choices and pitfalls that may severely hamper interpretation and generalization performance unless carefully considered. In this perspective article, we aim to motivate the use of machine learning schizotypy research. To this end, we describe common data processing steps while commenting on best practices and procedures. First, we introduce the important role of schizotypy to motivate the importance of reliable classification, and summarize existing machine learning literature on schizotypy. Then, we describe procedures for extraction of features based on fMRI data, including statistical parametric mapping, parcellation, complex network analysis, and decomposition methods, as well as classification with a special focus on support vector classification and deep learning. We provide more detailed descriptions and software as supplementary material. Finally, we present current challenges in machine learning for classification of schizotypy and comment on future trends and perspectives.

Improved Online Support Vector Machines Spam Filtering Using String Kernels

NASA Astrophysics Data System (ADS)

Amayri, Ola; Bouguila, Nizar

A major bottleneck in electronic communications is the enormous dissemination of spam emails. Developing of suitable filters that can adequately capture those emails and achieve high performance rate become a main concern. Support vector machines (SVMs) have made a large contribution to the development of spam email filtering. Based on SVMs, the crucial problems in email classification are feature mapping of input emails and the choice of the kernels. In this paper, we present thorough investigation of several distance-based kernels and propose the use of string kernels and prove its efficiency in blocking spam emails. We detail a feature mapping variants in text classification (TC) that yield improved performance for the standard SVMs in filtering task. Furthermore, to cope for realtime scenarios we propose an online active framework for spam filtering.

Implementation of a smartphone wireless accelerometer platform for establishing deep brain stimulation treatment efficacy of essential tremor with machine learning.

PubMed

LeMoyne, Robert; Tomycz, Nestor; Mastroianni, Timothy; McCandless, Cyrus; Cozza, Michael; Peduto, David

2015-01-01

Essential tremor (ET) is a highly prevalent movement disorder. Patients with ET exhibit a complex progressive and disabling tremor, and medical management often fails. Deep brain stimulation (DBS) has been successfully applied to this disorder, however there has been no quantifiable way to measure tremor severity or treatment efficacy in this patient population. The quantified amelioration of kinetic tremor via DBS is herein demonstrated through the application of a smartphone (iPhone) as a wireless accelerometer platform. The recorded acceleration signal can be obtained at a setting of the subject's convenience and conveyed by wireless transmission through the Internet for post-processing anywhere in the world. Further post-processing of the acceleration signal can be classified through a machine learning application, such as the support vector machine. Preliminary application of deep brain stimulation with a smartphone for acquisition of a feature set and machine learning for classification has been successfully applied. The support vector machine achieved 100% classification between deep brain stimulation in `on' and `off' mode based on the recording of an accelerometer signal through a smartphone as a wireless accelerometer platform.

Prediction of plant pre-microRNAs and their microRNAs in genome-scale sequences using structure-sequence features and support vector machine.

PubMed

Meng, Jun; Liu, Dong; Sun, Chao; Luan, Yushi

2014-12-30

MicroRNAs (miRNAs) are a family of non-coding RNAs approximately 21 nucleotides in length that play pivotal roles at the post-transcriptional level in animals, plants and viruses. These molecules silence their target genes by degrading transcription or suppressing translation. Studies have shown that miRNAs are involved in biological responses to a variety of biotic and abiotic stresses. Identification of these molecules and their targets can aid the understanding of regulatory processes. Recently, prediction methods based on machine learning have been widely used for miRNA prediction. However, most of these methods were designed for mammalian miRNA prediction, and few are available for predicting miRNAs in the pre-miRNAs of specific plant species. Although the complete Solanum lycopersicum genome has been published, only 77 Solanum lycopersicum miRNAs have been identified, far less than the estimated number. Therefore, it is essential to develop a prediction method based on machine learning to identify new plant miRNAs. A novel classification model based on a support vector machine (SVM) was trained to identify real and pseudo plant pre-miRNAs together with their miRNAs. An initial set of 152 novel features related to sequential structures was used to train the model. By applying feature selection, we obtained the best subset of 47 features for use with the Back Support Vector Machine-Recursive Feature Elimination (B-SVM-RFE) method for the classification of plant pre-miRNAs. Using this method, 63 features were obtained for plant miRNA classification. We then developed an integrated classification model, miPlantPreMat, which comprises MiPlantPre and MiPlantMat, to identify plant pre-miRNAs and their miRNAs. This model achieved approximately 90% accuracy using plant datasets from nine plant species, including Arabidopsis thaliana, Glycine max, Oryza sativa, Physcomitrella patens, Medicago truncatula, Sorghum bicolor, Arabidopsis lyrata, Zea mays and Solanum lycopersicum. Using miPlantPreMat, 522 Solanum lycopersicum miRNAs were identified in the Solanum lycopersicum genome sequence. We developed an integrated classification model, miPlantPreMat, based on structure-sequence features and SVM. MiPlantPreMat was used to identify both plant pre-miRNAs and the corresponding mature miRNAs. An improved feature selection method was proposed, resulting in high classification accuracy, sensitivity and specificity.

Time-reversal imaging for classification of submerged elastic targets via Gibbs sampling and the Relevance Vector Machine.

PubMed

Dasgupta, Nilanjan; Carin, Lawrence

2005-04-01

Time-reversal imaging (TRI) is analogous to matched-field processing, although TRI is typically very wideband and is appropriate for subsequent target classification (in addition to localization). Time-reversal techniques, as applied to acoustic target classification, are highly sensitive to channel mismatch. Hence, it is crucial to estimate the channel parameters before time-reversal imaging is performed. The channel-parameter statistics are estimated here by applying a geoacoustic inversion technique based on Gibbs sampling. The maximum a posteriori (MAP) estimate of the channel parameters are then used to perform time-reversal imaging. Time-reversal implementation requires a fast forward model, implemented here by a normal-mode framework. In addition to imaging, extraction of features from the time-reversed images is explored, with these applied to subsequent target classification. The classification of time-reversed signatures is performed by the relevance vector machine (RVM). The efficacy of the technique is analyzed on simulated in-channel data generated by a free-field finite element method (FEM) code, in conjunction with a channel propagation model, wherein the final classification performance is demonstrated to be relatively insensitive to the associated channel parameters. The underlying theory of Gibbs sampling and TRI are presented along with the feature extraction and target classification via the RVM.

Machine-learning in grading of gliomas based on multi-parametric magnetic resonance imaging at 3T.

PubMed

Citak-Er, Fusun; Firat, Zeynep; Kovanlikaya, Ilhami; Ture, Ugur; Ozturk-Isik, Esin

2018-06-15

The objective of this study was to assess the contribution of multi-parametric (mp) magnetic resonance imaging (MRI) quantitative features in the machine learning-based grading of gliomas with a multi-region-of-interests approach. Forty-three patients who were newly diagnosed as having a glioma were included in this study. The patients were scanned prior to any therapy using a standard brain tumor magnetic resonance (MR) imaging protocol that included T1 and T2-weighted, diffusion-weighted, diffusion tensor, MR perfusion and MR spectroscopic imaging. Three different regions-of-interest were drawn for each subject to encompass tumor, immediate tumor periphery, and distant peritumoral edema/normal. The normalized mp-MRI features were used to build machine-learning models for differentiating low-grade gliomas (WHO grades I and II) from high grades (WHO grades III and IV). In order to assess the contribution of regional mp-MRI quantitative features to the classification models, a support vector machine-based recursive feature elimination method was applied prior to classification. A machine-learning model based on support vector machine algorithm with linear kernel achieved an accuracy of 93.0%, a specificity of 86.7%, and a sensitivity of 96.4% for the grading of gliomas using ten-fold cross validation based on the proposed subset of the mp-MRI features. In this study, machine-learning based on multiregional and multi-parametric MRI data has proven to be an important tool in grading glial tumors accurately even in this limited patient population. Future studies are needed to investigate the use of machine learning algorithms for brain tumor classification in a larger patient cohort. Copyright © 2018. Published by Elsevier Ltd.

Control-group feature normalization for multivariate pattern analysis of structural MRI data using the support vector machine.

PubMed

Linn, Kristin A; Gaonkar, Bilwaj; Satterthwaite, Theodore D; Doshi, Jimit; Davatzikos, Christos; Shinohara, Russell T

2016-05-15

Normalization of feature vector values is a common practice in machine learning. Generally, each feature value is standardized to the unit hypercube or by normalizing to zero mean and unit variance. Classification decisions based on support vector machines (SVMs) or by other methods are sensitive to the specific normalization used on the features. In the context of multivariate pattern analysis using neuroimaging data, standardization effectively up- and down-weights features based on their individual variability. Since the standard approach uses the entire data set to guide the normalization, it utilizes the total variability of these features. This total variation is inevitably dependent on the amount of marginal separation between groups. Thus, such a normalization may attenuate the separability of the data in high dimensional space. In this work we propose an alternate approach that uses an estimate of the control-group standard deviation to normalize features before training. We study our proposed approach in the context of group classification using structural MRI data. We show that control-based normalization leads to better reproducibility of estimated multivariate disease patterns and improves the classifier performance in many cases. Copyright © 2016 Elsevier Inc. All rights reserved.

Relevance Vector Machine Learning for Neonate Pain Intensity Assessment Using Digital Imaging

PubMed Central

Gholami, Behnood; Tannenbaum, Allen R.

2011-01-01

Pain assessment in patients who are unable to verbally communicate is a challenging problem. The fundamental limitations in pain assessment in neonates stem from subjective assessment criteria, rather than quantifiable and measurable data. This often results in poor quality and inconsistent treatment of patient pain management. Recent advancements in pattern recognition techniques using relevance vector machine (RVM) learning techniques can assist medical staff in assessing pain by constantly monitoring the patient and providing the clinician with quantifiable data for pain management. The RVM classification technique is a Bayesian extension of the support vector machine (SVM) algorithm, which achieves comparable performance to SVM while providing posterior probabilities for class memberships and a sparser model. If classes represent “pure” facial expressions (i.e., extreme expressions that an observer can identify with a high degree of confidence), then the posterior probability of the membership of some intermediate facial expression to a class can provide an estimate of the intensity of such an expression. In this paper, we use the RVM classification technique to distinguish pain from nonpain in neonates as well as assess their pain intensity levels. We also correlate our results with the pain intensity assessed by expert and nonexpert human examiners. PMID:20172803

CARSVM: a class association rule-based classification framework and its application to gene expression data.

PubMed

Kianmehr, Keivan; Alhajj, Reda

2008-09-01

In this study, we aim at building a classification framework, namely the CARSVM model, which integrates association rule mining and support vector machine (SVM). The goal is to benefit from advantages of both, the discriminative knowledge represented by class association rules and the classification power of the SVM algorithm, to construct an efficient and accurate classifier model that improves the interpretability problem of SVM as a traditional machine learning technique and overcomes the efficiency issues of associative classification algorithms. In our proposed framework: instead of using the original training set, a set of rule-based feature vectors, which are generated based on the discriminative ability of class association rules over the training samples, are presented to the learning component of the SVM algorithm. We show that rule-based feature vectors present a high-qualified source of discrimination knowledge that can impact substantially the prediction power of SVM and associative classification techniques. They provide users with more conveniences in terms of understandability and interpretability as well. We have used four datasets from UCI ML repository to evaluate the performance of the developed system in comparison with five well-known existing classification methods. Because of the importance and popularity of gene expression analysis as real world application of the classification model, we present an extension of CARSVM combined with feature selection to be applied to gene expression data. Then, we describe how this combination will provide biologists with an efficient and understandable classifier model. The reported test results and their biological interpretation demonstrate the applicability, efficiency and effectiveness of the proposed model. From the results, it can be concluded that a considerable increase in classification accuracy can be obtained when the rule-based feature vectors are integrated in the learning process of the SVM algorithm. In the context of applicability, according to the results obtained from gene expression analysis, we can conclude that the CARSVM system can be utilized in a variety of real world applications with some adjustments.

Development of a classification method for a crack on a pavement surface images using machine learning

NASA Astrophysics Data System (ADS)

Hizukuri, Akiyoshi; Nagata, Takeshi

2017-03-01

The purpose of this study is to develop a classification method for a crack on a pavement surface image using machine learning to reduce a maintenance fee. Our database consists of 3500 pavement surface images. This includes 800 crack and 2700 normal pavement surface images. The pavement surface images first are decomposed into several sub-images using a discrete wavelet transform (DWT) decomposition. We then calculate the wavelet sub-band histogram from each several sub-images at each level. The support vector machine (SVM) with computed wavelet sub-band histogram is employed for distinguishing between a crack and normal pavement surface images. The accuracies of the proposed classification method are 85.3% for crack and 84.4% for normal pavement images. The proposed classification method achieved high performance. Therefore, the proposed method would be useful in maintenance inspection.

Improving urban land use and land cover classification from high-spatial-resolution hyperspectral imagery using contextual information

USDA-ARS?s Scientific Manuscript database

In this paper, we propose approaches to improve the pixel-based support vector machine (SVM) classification for urban land use and land cover (LULC) mapping from airborne hyperspectral imagery with high spatial resolution. Class spatial neighborhood relationship is used to correct the misclassified ...

Gender classification from face images by using local binary pattern and gray-level co-occurrence matrix

NASA Astrophysics Data System (ADS)

Uzbaş, Betül; Arslan, Ahmet

2018-04-01

Gender is an important step for human computer interactive processes and identification. Human face image is one of the important sources to determine gender. In the present study, gender classification is performed automatically from facial images. In order to classify gender, we propose a combination of features that have been extracted face, eye and lip regions by using a hybrid method of Local Binary Pattern and Gray-Level Co-Occurrence Matrix. The features have been extracted from automatically obtained face, eye and lip regions. All of the extracted features have been combined and given as input parameters to classification methods (Support Vector Machine, Artificial Neural Networks, Naive Bayes and k-Nearest Neighbor methods) for gender classification. The Nottingham Scan face database that consists of the frontal face images of 100 people (50 male and 50 female) is used for this purpose. As the result of the experimental studies, the highest success rate has been achieved as 98% by using Support Vector Machine. The experimental results illustrate the efficacy of our proposed method.

EEG-based driver fatigue detection using hybrid deep generic model.

PubMed

Phyo Phyo San; Sai Ho Ling; Rifai Chai; Tran, Yvonne; Craig, Ashley; Hung Nguyen

2016-08-01

Classification of electroencephalography (EEG)-based application is one of the important process for biomedical engineering. Driver fatigue is a major case of traffic accidents worldwide and considered as a significant problem in recent decades. In this paper, a hybrid deep generic model (DGM)-based support vector machine is proposed for accurate detection of driver fatigue. Traditionally, a probabilistic DGM with deep architecture is quite good at learning invariant features, but it is not always optimal for classification due to its trainable parameters are in the middle layer. Alternatively, Support Vector Machine (SVM) itself is unable to learn complicated invariance, but produces good decision surface when applied to well-behaved features. Consolidating unsupervised high-level feature extraction techniques, DGM and SVM classification makes the integrated framework stronger and enhance mutually in feature extraction and classification. The experimental results showed that the proposed DBN-based driver fatigue monitoring system achieves better testing accuracy of 73.29 % with 91.10 % sensitivity and 55.48 % specificity. In short, the proposed hybrid DGM-based SVM is an effective method for the detection of driver fatigue in EEG.

Support vector machine classification of major depressive disorder using diffusion-weighted neuroimaging and graph theory.

PubMed

Sacchet, Matthew D; Prasad, Gautam; Foland-Ross, Lara C; Thompson, Paul M; Gotlib, Ian H

2015-01-01

Recently, there has been considerable interest in understanding brain networks in major depressive disorder (MDD). Neural pathways can be tracked in the living brain using diffusion-weighted imaging (DWI); graph theory can then be used to study properties of the resulting fiber networks. To date, global abnormalities have not been reported in tractography-based graph metrics in MDD, so we used a machine learning approach based on "support vector machines" to differentiate depressed from healthy individuals based on multiple brain network properties. We also assessed how important specific graph metrics were for this differentiation. Finally, we conducted a local graph analysis to identify abnormal connectivity at specific nodes of the network. We were able to classify depression using whole-brain graph metrics. Small-worldness was the most useful graph metric for classification. The right pars orbitalis, right inferior parietal cortex, and left rostral anterior cingulate all showed abnormal network connectivity in MDD. This is the first use of structural global graph metrics to classify depressed individuals. These findings highlight the importance of future research to understand network properties in depression across imaging modalities, improve classification results, and relate network alterations to psychiatric symptoms, medication, and comorbidities.

Recursive feature selection with significant variables of support vectors.

PubMed

Tsai, Chen-An; Huang, Chien-Hsun; Chang, Ching-Wei; Chen, Chun-Houh

2012-01-01

The development of DNA microarray makes researchers screen thousands of genes simultaneously and it also helps determine high- and low-expression level genes in normal and disease tissues. Selecting relevant genes for cancer classification is an important issue. Most of the gene selection methods use univariate ranking criteria and arbitrarily choose a threshold to choose genes. However, the parameter setting may not be compatible to the selected classification algorithms. In this paper, we propose a new gene selection method (SVM-t) based on the use of t-statistics embedded in support vector machine. We compared the performance to two similar SVM-based methods: SVM recursive feature elimination (SVMRFE) and recursive support vector machine (RSVM). The three methods were compared based on extensive simulation experiments and analyses of two published microarray datasets. In the simulation experiments, we found that the proposed method is more robust in selecting informative genes than SVMRFE and RSVM and capable to attain good classification performance when the variations of informative and noninformative genes are different. In the analysis of two microarray datasets, the proposed method yields better performance in identifying fewer genes with good prediction accuracy, compared to SVMRFE and RSVM.

Automatic classification of protein structures using physicochemical parameters.

PubMed

Mohan, Abhilash; Rao, M Divya; Sunderrajan, Shruthi; Pennathur, Gautam

2014-09-01

Protein classification is the first step to functional annotation; SCOP and Pfam databases are currently the most relevant protein classification schemes. However, the disproportion in the number of three dimensional (3D) protein structures generated versus their classification into relevant superfamilies/families emphasizes the need for automated classification schemes. Predicting function of novel proteins based on sequence information alone has proven to be a major challenge. The present study focuses on the use of physicochemical parameters in conjunction with machine learning algorithms (Naive Bayes, Decision Trees, Random Forest and Support Vector Machines) to classify proteins into their respective SCOP superfamily/Pfam family, using sequence derived information. Spectrophores™, a 1D descriptor of the 3D molecular field surrounding a structure was used as a benchmark to compare the performance of the physicochemical parameters. The machine learning algorithms were modified to select features based on information gain for each SCOP superfamily/Pfam family. The effect of combining physicochemical parameters and spectrophores on classification accuracy (CA) was studied. Machine learning algorithms trained with the physicochemical parameters consistently classified SCOP superfamilies and Pfam families with a classification accuracy above 90%, while spectrophores performed with a CA of around 85%. Feature selection improved classification accuracy for both physicochemical parameters and spectrophores based machine learning algorithms. Combining both attributes resulted in a marginal loss of performance. Physicochemical parameters were able to classify proteins from both schemes with classification accuracy ranging from 90-96%. These results suggest the usefulness of this method in classifying proteins from amino acid sequences.

Spatial-spectral blood cell classification with microscopic hyperspectral imagery

NASA Astrophysics Data System (ADS)

Ran, Qiong; Chang, Lan; Li, Wei; Xu, Xiaofeng

2017-10-01

Microscopic hyperspectral images provide a new way for blood cell examination. The hyperspectral imagery can greatly facilitate the classification of different blood cells. In this paper, the microscopic hyperspectral images are acquired by connecting the microscope and the hyperspectral imager, and then tested for blood cell classification. For combined use of the spectral and spatial information provided by hyperspectral images, a spatial-spectral classification method is improved from the classical extreme learning machine (ELM) by integrating spatial context into the image classification task with Markov random field (MRF) model. Comparisons are done among ELM, ELM-MRF, support vector machines(SVM) and SVMMRF methods. Results show the spatial-spectral classification methods(ELM-MRF, SVM-MRF) perform better than pixel-based methods(ELM, SVM), and the proposed ELM-MRF has higher precision and show more accurate location of cells.

Automatic event detection in low SNR microseismic signals based on multi-scale permutation entropy and a support vector machine

NASA Astrophysics Data System (ADS)

Jia, Rui-Sheng; Sun, Hong-Mei; Peng, Yan-Jun; Liang, Yong-Quan; Lu, Xin-Ming

2017-07-01

Microseismic monitoring is an effective means for providing early warning of rock or coal dynamical disasters, and its first step is microseismic event detection, although low SNR microseismic signals often cannot effectively be detected by routine methods. To solve this problem, this paper presents permutation entropy and a support vector machine to detect low SNR microseismic events. First, an extraction method of signal features based on multi-scale permutation entropy is proposed by studying the influence of the scale factor on the signal permutation entropy. Second, the detection model of low SNR microseismic events based on the least squares support vector machine is built by performing a multi-scale permutation entropy calculation for the collected vibration signals, constructing a feature vector set of signals. Finally, a comparative analysis of the microseismic events and noise signals in the experiment proves that the different characteristics of the two can be fully expressed by using multi-scale permutation entropy. The detection model of microseismic events combined with the support vector machine, which has the features of high classification accuracy and fast real-time algorithms, can meet the requirements of online, real-time extractions of microseismic events.

Support Vector Machine algorithm for regression and classification

DOE Office of Scientific and Technical Information (OSTI.GOV)

Yu, Chenggang; Zavaljevski, Nela

2001-08-01

The software is an implementation of the Support Vector Machine (SVM) algorithm that was invented and developed by Vladimir Vapnik and his co-workers at AT&T Bell Laboratories. The specific implementation reported here is an Active Set method for solving a quadratic optimization problem that forms the major part of any SVM program. The implementation is tuned to specific constraints generated in the SVM learning. Thus, it is more efficient than general-purpose quadratic optimization programs. A decomposition method has been implemented in the software that enables processing large data sets. The size of the learning data is virtually unlimited by themore » capacity of the computer physical memory. The software is flexible and extensible. Two upper bounds are implemented to regulate the SVM learning for classification, which allow users to adjust the false positive and false negative rates. The software can be used either as a standalone, general-purpose SVM regression or classification program, or be embedded into a larger software system.« less

Binary classification of aqueous solubility using support vector machines with reduction and recombination feature selection.

PubMed

Cheng, Tiejun; Li, Qingliang; Wang, Yanli; Bryant, Stephen H

2011-02-28

Aqueous solubility is recognized as a critical parameter in both the early- and late-stage drug discovery. Therefore, in silico modeling of solubility has attracted extensive interests in recent years. Most previous studies have been limited in using relatively small data sets with limited diversity, which in turn limits the predictability of derived models. In this work, we present a support vector machines model for the binary classification of solubility by taking advantage of the largest known public data set that contains over 46 000 compounds with experimental solubility. Our model was optimized in combination with a reduction and recombination feature selection strategy. The best model demonstrated robust performance in both cross-validation and prediction of two independent test sets, indicating it could be a practical tool to select soluble compounds for screening, purchasing, and synthesizing. Moreover, our work may be used for comparative evaluation of solubility classification studies ascribe to the use of completely public resources.

Combination of support vector machine, artificial neural network and random forest for improving the classification of convective and stratiform rain using spectral features of SEVIRI data

NASA Astrophysics Data System (ADS)

Lazri, Mourad; Ameur, Soltane

2018-05-01

A model combining three classifiers, namely Support vector machine, Artificial neural network and Random forest (SAR) is designed for improving the classification of convective and stratiform rain. This model (SAR model) has been trained and then tested on a datasets derived from MSG-SEVIRI (Meteosat Second Generation-Spinning Enhanced Visible and Infrared Imager). Well-classified, mid-classified and misclassified pixels are determined from the combination of three classifiers. Mid-classified and misclassified pixels that are considered unreliable pixels are reclassified by using a novel training of the developed scheme. In this novel training, only the input data corresponding to the pixels in question to are used. This whole process is repeated a second time and applied to mid-classified and misclassified pixels separately. Learning and validation of the developed scheme are realized against co-located data observed by ground radar. The developed scheme outperformed different classifiers used separately and reached 97.40% of overall accuracy of classification.

Parameters selection in gene selection using Gaussian kernel support vector machines by genetic algorithm.

PubMed

Mao, Yong; Zhou, Xiao-Bo; Pi, Dao-Ying; Sun, You-Xian; Wong, Stephen T C

2005-10-01

In microarray-based cancer classification, gene selection is an important issue owing to the large number of variables and small number of samples as well as its non-linearity. It is difficult to get satisfying results by using conventional linear statistical methods. Recursive feature elimination based on support vector machine (SVM RFE) is an effective algorithm for gene selection and cancer classification, which are integrated into a consistent framework. In this paper, we propose a new method to select parameters of the aforementioned algorithm implemented with Gaussian kernel SVMs as better alternatives to the common practice of selecting the apparently best parameters by using a genetic algorithm to search for a couple of optimal parameter. Fast implementation issues for this method are also discussed for pragmatic reasons. The proposed method was tested on two representative hereditary breast cancer and acute leukaemia datasets. The experimental results indicate that the proposed method performs well in selecting genes and achieves high classification accuracies with these genes.

Automatic road sign detecion and classification based on support vector machines and HOG descriptos

NASA Astrophysics Data System (ADS)

Adam, A.; Ioannidis, C.

2014-05-01

This paper examines the detection and classification of road signs in color-images acquired by a low cost camera mounted on a moving vehicle. A new method for the detection and classification of road signs is proposed based on color based detection, in order to locate regions of interest. Then, a circular Hough transform is applied to complete detection taking advantage of the shape properties of the road signs. The regions of interest are finally represented using HOG descriptors and are fed into trained Support Vector Machines (SVMs) in order to be recognized. For the training procedure, a database with several training examples depicting Greek road sings has been developed. Many experiments have been conducted and are presented, to measure the efficiency of the proposed methodology especially under adverse weather conditions and poor illumination. For the experiments training datasets consisting of different number of examples were used and the results are presented, along with some possible extensions of this work.

Classification of inflammatory bowel diseases by means of Raman spectroscopic imaging of epithelium cells

NASA Astrophysics Data System (ADS)

Bielecki, Christiane; Bocklitz, Thomas W.; Schmitt, Michael; Krafft, Christoph; Marquardt, Claudio; Gharbi, Akram; Knösel, Thomas; Stallmach, Andreas; Popp, Juergen

2012-07-01

We report on a Raman microspectroscopic characterization of the inflammatory bowel diseases (IBD) Crohn's disease (CD) and ulcerative colitis (UC). Therefore, Raman maps of human colon tissue sections were analyzed by utilizing innovative chemometric approaches. First, support vector machines were applied to highlight the tissue morphology (=Raman spectroscopic histopathology). In a second step, the biochemical tissue composition has been studied by analyzing the epithelium Raman spectra of sections of healthy control subjects (n=11), subjects with CD (n=14), and subjects with UC (n=13). These three groups exhibit significantly different molecular specific Raman signatures, allowing establishment of a classifier (support-vector-machine). By utilizing this classifier it was possible to separate between healthy control patients, patients with CD, and patients with UC with an accuracy of 98.90%. The automatic design of both classification steps (visualization of the tissue morphology and molecular classification of IBD) paves the way for an objective clinical diagnosis of IBD by means of Raman spectroscopy in combination with chemometric approaches.

Research on Remote Sensing Image Classification Based on Feature Level Fusion

NASA Astrophysics Data System (ADS)

Yuan, L.; Zhu, G.

2018-04-01

Remote sensing image classification, as an important direction of remote sensing image processing and application, has been widely studied. However, in the process of existing classification algorithms, there still exists the phenomenon of misclassification and missing points, which leads to the final classification accuracy is not high. In this paper, we selected Sentinel-1A and Landsat8 OLI images as data sources, and propose a classification method based on feature level fusion. Compare three kind of feature level fusion algorithms (i.e., Gram-Schmidt spectral sharpening, Principal Component Analysis transform and Brovey transform), and then select the best fused image for the classification experimental. In the classification process, we choose four kinds of image classification algorithms (i.e. Minimum distance, Mahalanobis distance, Support Vector Machine and ISODATA) to do contrast experiment. We use overall classification precision and Kappa coefficient as the classification accuracy evaluation criteria, and the four classification results of fused image are analysed. The experimental results show that the fusion effect of Gram-Schmidt spectral sharpening is better than other methods. In four kinds of classification algorithms, the fused image has the best applicability to Support Vector Machine classification, the overall classification precision is 94.01 % and the Kappa coefficients is 0.91. The fused image with Sentinel-1A and Landsat8 OLI is not only have more spatial information and spectral texture characteristics, but also enhances the distinguishing features of the images. The proposed method is beneficial to improve the accuracy and stability of remote sensing image classification.

A Novel Extreme Learning Machine Classification Model for e-Nose Application Based on the Multiple Kernel Approach

PubMed Central

Jian, Yulin; Huang, Daoyu; Yan, Jia; Lu, Kun; Huang, Ying; Wen, Tailai; Zeng, Tanyue; Zhong, Shijie; Xie, Qilong

2017-01-01

A novel classification model, named the quantum-behaved particle swarm optimization (QPSO)-based weighted multiple kernel extreme learning machine (QWMK-ELM), is proposed in this paper. Experimental validation is carried out with two different electronic nose (e-nose) datasets. Being different from the existing multiple kernel extreme learning machine (MK-ELM) algorithms, the combination coefficients of base kernels are regarded as external parameters of single-hidden layer feedforward neural networks (SLFNs). The combination coefficients of base kernels, the model parameters of each base kernel, and the regularization parameter are optimized by QPSO simultaneously before implementing the kernel extreme learning machine (KELM) with the composite kernel function. Four types of common single kernel functions (Gaussian kernel, polynomial kernel, sigmoid kernel, and wavelet kernel) are utilized to constitute different composite kernel functions. Moreover, the method is also compared with other existing classification methods: extreme learning machine (ELM), kernel extreme learning machine (KELM), k-nearest neighbors (KNN), support vector machine (SVM), multi-layer perceptron (MLP), radical basis function neural network (RBFNN), and probabilistic neural network (PNN). The results have demonstrated that the proposed QWMK-ELM outperforms the aforementioned methods, not only in precision, but also in efficiency for gas classification. PMID:28629202

Using support vector machines with tract-based spatial statistics for automated classification of Tourette syndrome children

NASA Astrophysics Data System (ADS)

Wen, Hongwei; Liu, Yue; Wang, Jieqiong; Zhang, Jishui; Peng, Yun; He, Huiguang

2016-03-01

Tourette syndrome (TS) is a developmental neuropsychiatric disorder with the cardinal symptoms of motor and vocal tics which emerges in early childhood and fluctuates in severity in later years. To date, the neural basis of TS is not fully understood yet and TS has a long-term prognosis that is difficult to accurately estimate. Few studies have looked at the potential of using diffusion tensor imaging (DTI) in conjunction with machine learning algorithms in order to automate the classification of healthy children and TS children. Here we apply Tract-Based Spatial Statistics (TBSS) method to 44 TS children and 48 age and gender matched healthy children in order to extract the diffusion values from each voxel in the white matter (WM) skeleton, and a feature selection algorithm (ReliefF) was used to select the most salient voxels for subsequent classification with support vector machine (SVM). We use a nested cross validation to yield an unbiased assessment of the classification method and prevent overestimation. The accuracy (88.04%), sensitivity (88.64%) and specificity (87.50%) were achieved in our method as peak performance of the SVM classifier was achieved using the axial diffusion (AD) metric, demonstrating the potential of a joint TBSS and SVM pipeline for fast, objective classification of healthy and TS children. These results support that our methods may be useful for the early identification of subjects with TS, and hold promise for predicting prognosis and treatment outcome for individuals with TS.

Ambulatory activity classification with dendogram-based support vector machine: Application in lower-limb active exoskeleton.

PubMed

Mazumder, Oishee; Kundu, Ananda Sankar; Lenka, Prasanna Kumar; Bhaumik, Subhasis

2016-10-01

Ambulatory activity classification is an active area of research for controlling and monitoring state initiation, termination, and transition in mobility assistive devices such as lower-limb exoskeletons. State transition of lower-limb exoskeletons reported thus far are achieved mostly through the use of manual switches or state machine-based logic. In this paper, we propose a postural activity classifier using a 'dendogram-based support vector machine' (DSVM) which can be used to control a lower-limb exoskeleton. A pressure sensor-based wearable insole and two six-axis inertial measurement units (IMU) have been used for recognising two static and seven dynamic postural activities: sit, stand, and sit-to-stand, stand-to-sit, level walk, fast walk, slope walk, stair ascent and stair descent. Most of the ambulatory activities are periodic in nature and have unique patterns of response. The proposed classification algorithm involves the recognition of activity patterns on the basis of the periodic shape of trajectories. Polynomial coefficients extracted from the hip angle trajectory and the centre-of-pressure (CoP) trajectory during an activity cycle are used as features to classify dynamic activities. The novelty of this paper lies in finding suitable instrumentation, developing post-processing techniques, and selecting shape-based features for ambulatory activity classification. The proposed activity classifier is used to identify the activity states of a lower-limb exoskeleton. The DSVM classifier algorithm achieved an overall classification accuracy of 95.2%. Copyright © 2016 Elsevier B.V. All rights reserved.

A Support Vector Machine-Based Gender Identification Using Speech Signal

NASA Astrophysics Data System (ADS)

Lee, Kye-Hwan; Kang, Sang-Ick; Kim, Deok-Hwan; Chang, Joon-Hyuk

We propose an effective voice-based gender identification method using a support vector machine (SVM). The SVM is a binary classification algorithm that classifies two groups by finding the voluntary nonlinear boundary in a feature space and is known to yield high classification performance. In the present work, we compare the identification performance of the SVM with that of a Gaussian mixture model (GMM)-based method using the mel frequency cepstral coefficients (MFCC). A novel approach of incorporating a features fusion scheme based on a combination of the MFCC and the fundamental frequency is proposed with the aim of improving the performance of gender identification. Experimental results demonstrate that the gender identification performance using the SVM is significantly better than that of the GMM-based scheme. Moreover, the performance is substantially improved when the proposed features fusion technique is applied.

Information extraction with object based support vector machines and vegetation indices

NASA Astrophysics Data System (ADS)

Ustuner, Mustafa; Abdikan, Saygin; Balik Sanli, Fusun

2016-07-01

Information extraction through remote sensing data is important for policy and decision makers as extracted information provide base layers for many application of real world. Classification of remotely sensed data is the one of the most common methods of extracting information however it is still a challenging issue because several factors are affecting the accuracy of the classification. Resolution of the imagery, number and homogeneity of land cover classes, purity of training data and characteristic of adopted classifiers are just some of these challenging factors. Object based image classification has some superiority than pixel based classification for high resolution images since it uses geometry and structure information besides spectral information. Vegetation indices are also commonly used for the classification process since it provides additional spectral information for vegetation, forestry and agricultural areas. In this study, the impacts of the Normalized Difference Vegetation Index (NDVI) and Normalized Difference Red Edge Index (NDRE) on the classification accuracy of RapidEye imagery were investigated. Object based Support Vector Machines were implemented for the classification of crop types for the study area located in Aegean region of Turkey. Results demonstrated that the incorporation of NDRE increase the classification accuracy from 79,96% to 86,80% as overall accuracy, however NDVI decrease the classification accuracy from 79,96% to 78,90%. Moreover it is proven than object based classification with RapidEye data give promising results for crop type mapping and analysis.

Fuzzy support vector machine for microarray imbalanced data classification

NASA Astrophysics Data System (ADS)

Ladayya, Faroh; Purnami, Santi Wulan; Irhamah

2017-11-01

DNA microarrays are data containing gene expression with small sample sizes and high number of features. Furthermore, imbalanced classes is a common problem in microarray data. This occurs when a dataset is dominated by a class which have significantly more instances than the other minority classes. Therefore, it is needed a classification method that solve the problem of high dimensional and imbalanced data. Support Vector Machine (SVM) is one of the classification methods that is capable of handling large or small samples, nonlinear, high dimensional, over learning and local minimum issues. SVM has been widely applied to DNA microarray data classification and it has been shown that SVM provides the best performance among other machine learning methods. However, imbalanced data will be a problem because SVM treats all samples in the same importance thus the results is bias for minority class. To overcome the imbalanced data, Fuzzy SVM (FSVM) is proposed. This method apply a fuzzy membership to each input point and reformulate the SVM such that different input points provide different contributions to the classifier. The minority classes have large fuzzy membership so FSVM can pay more attention to the samples with larger fuzzy membership. Given DNA microarray data is a high dimensional data with a very large number of features, it is necessary to do feature selection first using Fast Correlation based Filter (FCBF). In this study will be analyzed by SVM, FSVM and both methods by applying FCBF and get the classification performance of them. Based on the overall results, FSVM on selected features has the best classification performance compared to SVM.

The classification of the patients with pulmonary diseases using breath air samples spectral analysis

NASA Astrophysics Data System (ADS)

Kistenev, Yury V.; Borisov, Alexey V.; Kuzmin, Dmitry A.; Bulanova, Anna A.

2016-08-01

Technique of exhaled breath sampling is discussed. The procedure of wavelength auto-calibration is proposed and tested. Comparison of the experimental data with the model absorption spectra of 5% CO2 is conducted. The classification results of three study groups obtained by using support vector machine and principal component analysis methods are presented.

Support vector machine in machine condition monitoring and fault diagnosis

NASA Astrophysics Data System (ADS)

Widodo, Achmad; Yang, Bo-Suk

2007-08-01

Recently, the issue of machine condition monitoring and fault diagnosis as a part of maintenance system became global due to the potential advantages to be gained from reduced maintenance costs, improved productivity and increased machine availability. This paper presents a survey of machine condition monitoring and fault diagnosis using support vector machine (SVM). It attempts to summarize and review the recent research and developments of SVM in machine condition monitoring and diagnosis. Numerous methods have been developed based on intelligent systems such as artificial neural network, fuzzy expert system, condition-based reasoning, random forest, etc. However, the use of SVM for machine condition monitoring and fault diagnosis is still rare. SVM has excellent performance in generalization so it can produce high accuracy in classification for machine condition monitoring and diagnosis. Until 2006, the use of SVM in machine condition monitoring and fault diagnosis is tending to develop towards expertise orientation and problem-oriented domain. Finally, the ability to continually change and obtain a novel idea for machine condition monitoring and fault diagnosis using SVM will be future works.

Classification of highly unbalanced CYP450 data of drugs using cost sensitive machine learning techniques.

PubMed

Eitrich, T; Kless, A; Druska, C; Meyer, W; Grotendorst, J

2007-01-01

In this paper, we study the classifications of unbalanced data sets of drugs. As an example we chose a data set of 2D6 inhibitors of cytochrome P450. The human cytochrome P450 2D6 isoform plays a key role in the metabolism of many drugs in the preclinical drug discovery process. We have collected a data set from annotated public data and calculated physicochemical properties with chemoinformatics methods. On top of this data, we have built classifiers based on machine learning methods. Data sets with different class distributions lead to the effect that conventional machine learning methods are biased toward the larger class. To overcome this problem and to obtain sensitive but also accurate classifiers we combine machine learning and feature selection methods with techniques addressing the problem of unbalanced classification, such as oversampling and threshold moving. We have used our own implementation of a support vector machine algorithm as well as the maximum entropy method. Our feature selection is based on the unsupervised McCabe method. The classification results from our test set are compared structurally with compounds from the training set. We show that the applied algorithms enable the effective high throughput in silico classification of potential drug candidates.

A Fast Reduced Kernel Extreme Learning Machine.

PubMed

Deng, Wan-Yu; Ong, Yew-Soon; Zheng, Qing-Hua

2016-04-01

In this paper, we present a fast and accurate kernel-based supervised algorithm referred to as the Reduced Kernel Extreme Learning Machine (RKELM). In contrast to the work on Support Vector Machine (SVM) or Least Square SVM (LS-SVM), which identifies the support vectors or weight vectors iteratively, the proposed RKELM randomly selects a subset of the available data samples as support vectors (or mapping samples). By avoiding the iterative steps of SVM, significant cost savings in the training process can be readily attained, especially on Big datasets. RKELM is established based on the rigorous proof of universal learning involving reduced kernel-based SLFN. In particular, we prove that RKELM can approximate any nonlinear functions accurately under the condition of support vectors sufficiency. Experimental results on a wide variety of real world small instance size and large instance size applications in the context of binary classification, multi-class problem and regression are then reported to show that RKELM can perform at competitive level of generalized performance as the SVM/LS-SVM at only a fraction of the computational effort incurred. Copyright © 2015 Elsevier Ltd. All rights reserved.

A Comparison of Supervised Machine Learning Algorithms and Feature Vectors for MS Lesion Segmentation Using Multimodal Structural MRI

PubMed Central

Sweeney, Elizabeth M.; Vogelstein, Joshua T.; Cuzzocreo, Jennifer L.; Calabresi, Peter A.; Reich, Daniel S.; Crainiceanu, Ciprian M.; Shinohara, Russell T.

2014-01-01

Machine learning is a popular method for mining and analyzing large collections of medical data. We focus on a particular problem from medical research, supervised multiple sclerosis (MS) lesion segmentation in structural magnetic resonance imaging (MRI). We examine the extent to which the choice of machine learning or classification algorithm and feature extraction function impacts the performance of lesion segmentation methods. As quantitative measures derived from structural MRI are important clinical tools for research into the pathophysiology and natural history of MS, the development of automated lesion segmentation methods is an active research field. Yet, little is known about what drives performance of these methods. We evaluate the performance of automated MS lesion segmentation methods, which consist of a supervised classification algorithm composed with a feature extraction function. These feature extraction functions act on the observed T1-weighted (T1-w), T2-weighted (T2-w) and fluid-attenuated inversion recovery (FLAIR) MRI voxel intensities. Each MRI study has a manual lesion segmentation that we use to train and validate the supervised classification algorithms. Our main finding is that the differences in predictive performance are due more to differences in the feature vectors, rather than the machine learning or classification algorithms. Features that incorporate information from neighboring voxels in the brain were found to increase performance substantially. For lesion segmentation, we conclude that it is better to use simple, interpretable, and fast algorithms, such as logistic regression, linear discriminant analysis, and quadratic discriminant analysis, and to develop the features to improve performance. PMID:24781953

A comparison of supervised machine learning algorithms and feature vectors for MS lesion segmentation using multimodal structural MRI.

PubMed

Sweeney, Elizabeth M; Vogelstein, Joshua T; Cuzzocreo, Jennifer L; Calabresi, Peter A; Reich, Daniel S; Crainiceanu, Ciprian M; Shinohara, Russell T

2014-01-01

Machine learning is a popular method for mining and analyzing large collections of medical data. We focus on a particular problem from medical research, supervised multiple sclerosis (MS) lesion segmentation in structural magnetic resonance imaging (MRI). We examine the extent to which the choice of machine learning or classification algorithm and feature extraction function impacts the performance of lesion segmentation methods. As quantitative measures derived from structural MRI are important clinical tools for research into the pathophysiology and natural history of MS, the development of automated lesion segmentation methods is an active research field. Yet, little is known about what drives performance of these methods. We evaluate the performance of automated MS lesion segmentation methods, which consist of a supervised classification algorithm composed with a feature extraction function. These feature extraction functions act on the observed T1-weighted (T1-w), T2-weighted (T2-w) and fluid-attenuated inversion recovery (FLAIR) MRI voxel intensities. Each MRI study has a manual lesion segmentation that we use to train and validate the supervised classification algorithms. Our main finding is that the differences in predictive performance are due more to differences in the feature vectors, rather than the machine learning or classification algorithms. Features that incorporate information from neighboring voxels in the brain were found to increase performance substantially. For lesion segmentation, we conclude that it is better to use simple, interpretable, and fast algorithms, such as logistic regression, linear discriminant analysis, and quadratic discriminant analysis, and to develop the features to improve performance.

An unbalanced spectra classification method based on entropy

NASA Astrophysics Data System (ADS)

Liu, Zhong-bao; Zhao, Wen-juan

2017-05-01

How to solve the problem of distinguishing the minority spectra from the majority of the spectra is quite important in astronomy. In view of this, an unbalanced spectra classification method based on entropy (USCM) is proposed in this paper to deal with the unbalanced spectra classification problem. USCM greatly improves the performances of the traditional classifiers on distinguishing the minority spectra as it takes the data distribution into consideration in the process of classification. However, its time complexity is exponential with the training size, and therefore, it can only deal with the problem of small- and medium-scale classification. How to solve the large-scale classification problem is quite important to USCM. It can be easily obtained by mathematical computation that the dual form of USCM is equivalent to the minimum enclosing ball (MEB), and core vector machine (CVM) is introduced, USCM based on CVM is proposed to deal with the large-scale classification problem. Several comparative experiments on the 4 subclasses of K-type spectra, 3 subclasses of F-type spectra and 3 subclasses of G-type spectra from Sloan Digital Sky Survey (SDSS) verify USCM and USCM based on CVM perform better than kNN (k nearest neighbor) and SVM (support vector machine) in dealing with the problem of rare spectra mining respectively on the small- and medium-scale datasets and the large-scale datasets.

Discriminant analysis for fast multiclass data classification through regularized kernel function approximation.

PubMed

Ghorai, Santanu; Mukherjee, Anirban; Dutta, Pranab K

2010-06-01

In this brief we have proposed the multiclass data classification by computationally inexpensive discriminant analysis through vector-valued regularized kernel function approximation (VVRKFA). VVRKFA being an extension of fast regularized kernel function approximation (FRKFA), provides the vector-valued response at single step. The VVRKFA finds a linear operator and a bias vector by using a reduced kernel that maps a pattern from feature space into the low dimensional label space. The classification of patterns is carried out in this low dimensional label subspace. A test pattern is classified depending on its proximity to class centroids. The effectiveness of the proposed method is experimentally verified and compared with multiclass support vector machine (SVM) on several benchmark data sets as well as on gene microarray data for multi-category cancer classification. The results indicate the significant improvement in both training and testing time compared to that of multiclass SVM with comparable testing accuracy principally in large data sets. Experiments in this brief also serve as comparison of performance of VVRKFA with stratified random sampling and sub-sampling.

Broiler chickens can benefit from machine learning: support vector machine analysis of observational epidemiological data

PubMed Central

Hepworth, Philip J.; Nefedov, Alexey V.; Muchnik, Ilya B.; Morgan, Kenton L.

2012-01-01

Machine-learning algorithms pervade our daily lives. In epidemiology, supervised machine learning has the potential for classification, diagnosis and risk factor identification. Here, we report the use of support vector machine learning to identify the features associated with hock burn on commercial broiler farms, using routinely collected farm management data. These data lend themselves to analysis using machine-learning techniques. Hock burn, dermatitis of the skin over the hock, is an important indicator of broiler health and welfare. Remarkably, this classifier can predict the occurrence of high hock burn prevalence with accuracy of 0.78 on unseen data, as measured by the area under the receiver operating characteristic curve. We also compare the results with those obtained by standard multi-variable logistic regression and suggest that this technique provides new insights into the data. This novel application of a machine-learning algorithm, embedded in poultry management systems could offer significant improvements in broiler health and welfare worldwide. PMID:22319115

Broiler chickens can benefit from machine learning: support vector machine analysis of observational epidemiological data.

PubMed

Hepworth, Philip J; Nefedov, Alexey V; Muchnik, Ilya B; Morgan, Kenton L

2012-08-07

Machine-learning algorithms pervade our daily lives. In epidemiology, supervised machine learning has the potential for classification, diagnosis and risk factor identification. Here, we report the use of support vector machine learning to identify the features associated with hock burn on commercial broiler farms, using routinely collected farm management data. These data lend themselves to analysis using machine-learning techniques. Hock burn, dermatitis of the skin over the hock, is an important indicator of broiler health and welfare. Remarkably, this classifier can predict the occurrence of high hock burn prevalence with accuracy of 0.78 on unseen data, as measured by the area under the receiver operating characteristic curve. We also compare the results with those obtained by standard multi-variable logistic regression and suggest that this technique provides new insights into the data. This novel application of a machine-learning algorithm, embedded in poultry management systems could offer significant improvements in broiler health and welfare worldwide.

Merged or monolithic? Using machine-learning to reconstruct the dynamical history of simulated star clusters

NASA Astrophysics Data System (ADS)

Pasquato, Mario; Chung, Chul

2016-05-01

Context. Machine-learning (ML) solves problems by learning patterns from data with limited or no human guidance. In astronomy, ML is mainly applied to large observational datasets, e.g. for morphological galaxy classification. Aims: We apply ML to gravitational N-body simulations of star clusters that are either formed by merging two progenitors or evolved in isolation, planning to later identify globular clusters (GCs) that may have a history of merging from observational data. Methods: We create mock-observations from simulated GCs, from which we measure a set of parameters (also called features in the machine-learning field). After carrying out dimensionality reduction on the feature space, the resulting datapoints are fed in to various classification algorithms. Using repeated random subsampling validation, we check whether the groups identified by the algorithms correspond to the underlying physical distinction between mergers and monolithically evolved simulations. Results: The three algorithms we considered (C5.0 trees, k-nearest neighbour, and support-vector machines) all achieve a test misclassification rate of about 10% without parameter tuning, with support-vector machines slightly outperforming the others. The first principal component of feature space correlates with cluster concentration. If we exclude it from the regression, the performance of the algorithms is only slightly reduced.

Classification of sodium MRI data of cartilage using machine learning.

PubMed

Madelin, Guillaume; Poidevin, Frederick; Makrymallis, Antonios; Regatte, Ravinder R

2015-11-01

To assess the possible utility of machine learning for classifying subjects with and subjects without osteoarthritis using sodium magnetic resonance imaging data. Theory: Support vector machine, k-nearest neighbors, naïve Bayes, discriminant analysis, linear regression, logistic regression, neural networks, decision tree, and tree bagging were tested. Sodium magnetic resonance imaging with and without fluid suppression by inversion recovery was acquired on the knee cartilage of 19 controls and 28 osteoarthritis patients. Sodium concentrations were measured in regions of interests in the knee for both acquisitions. Mean (MEAN) and standard deviation (STD) of these concentrations were measured in each regions of interest, and the minimum, maximum, and mean of these two measurements were calculated over all regions of interests for each subject. The resulting 12 variables per subject were used as predictors for classification. Either Min [STD] alone, or in combination with Mean [MEAN] or Min [MEAN], all from fluid suppressed data, were the best predictors with an accuracy >74%, mainly with linear logistic regression and linear support vector machine. Other good classifiers include discriminant analysis, linear regression, and naïve Bayes. Machine learning is a promising technique for classifying osteoarthritis patients and controls from sodium magnetic resonance imaging data. © 2014 Wiley Periodicals, Inc.

Automatic migraine classification via feature selection committee and machine learning techniques over imaging and questionnaire data.

PubMed

Garcia-Chimeno, Yolanda; Garcia-Zapirain, Begonya; Gomez-Beldarrain, Marian; Fernandez-Ruanova, Begonya; Garcia-Monco, Juan Carlos

2017-04-13

Feature selection methods are commonly used to identify subsets of relevant features to facilitate the construction of models for classification, yet little is known about how feature selection methods perform in diffusion tensor images (DTIs). In this study, feature selection and machine learning classification methods were tested for the purpose of automating diagnosis of migraines using both DTIs and questionnaire answers related to emotion and cognition - factors that influence of pain perceptions. We select 52 adult subjects for the study divided into three groups: control group (15), subjects with sporadic migraine (19) and subjects with chronic migraine and medication overuse (18). These subjects underwent magnetic resonance with diffusion tensor to see white matter pathway integrity of the regions of interest involved in pain and emotion. The tests also gather data about pathology. The DTI images and test results were then introduced into feature selection algorithms (Gradient Tree Boosting, L1-based, Random Forest and Univariate) to reduce features of the first dataset and classification algorithms (SVM (Support Vector Machine), Boosting (Adaboost) and Naive Bayes) to perform a classification of migraine group. Moreover we implement a committee method to improve the classification accuracy based on feature selection algorithms. When classifying the migraine group, the greatest improvements in accuracy were made using the proposed committee-based feature selection method. Using this approach, the accuracy of classification into three types improved from 67 to 93% when using the Naive Bayes classifier, from 90 to 95% with the support vector machine classifier, 93 to 94% in boosting. The features that were determined to be most useful for classification included are related with the pain, analgesics and left uncinate brain (connected with the pain and emotions). The proposed feature selection committee method improved the performance of migraine diagnosis classifiers compared to individual feature selection methods, producing a robust system that achieved over 90% accuracy in all classifiers. The results suggest that the proposed methods can be used to support specialists in the classification of migraines in patients undergoing magnetic resonance imaging.

Evaluation and recognition of skin images with aging by support vector machine

NASA Astrophysics Data System (ADS)

Hu, Liangjun; Wu, Shulian; Li, Hui

2016-10-01

Aging is a very important issue not only in dermatology, but also cosmetic science. Cutaneous aging involves both chronological and photoaging aging process. The evaluation and classification of aging is an important issue with the medical cosmetology workers nowadays. The purpose of this study is to assess chronological-age-related and photo-age-related of human skin. The texture features of skin surface skin, such as coarseness, contrast were analyzed by Fourier transform and Tamura. And the aim of it is to detect the object hidden in the skin texture in difference aging skin. Then, Support vector machine was applied to train the texture feature. The different age's states were distinguished by the support vector machine (SVM) classifier. The results help us to further understand the mechanism of different aging skin from texture feature and help us to distinguish the different aging states.

Extraction and classification of 3D objects from volumetric CT data

NASA Astrophysics Data System (ADS)

Song, Samuel M.; Kwon, Junghyun; Ely, Austin; Enyeart, John; Johnson, Chad; Lee, Jongkyu; Kim, Namho; Boyd, Douglas P.

2016-05-01

We propose an Automatic Threat Detection (ATD) algorithm for Explosive Detection System (EDS) using our multistage Segmentation Carving (SC) followed by Support Vector Machine (SVM) classifier. The multi-stage Segmentation and Carving (SC) step extracts all suspect 3-D objects. The feature vector is then constructed for all extracted objects and the feature vector is classified by the Support Vector Machine (SVM) previously learned using a set of ground truth threat and benign objects. The learned SVM classifier has shown to be effective in classification of different types of threat materials. The proposed ATD algorithm robustly deals with CT data that are prone to artifacts due to scatter, beam hardening as well as other systematic idiosyncrasies of the CT data. Furthermore, the proposed ATD algorithm is amenable for including newly emerging threat materials as well as for accommodating data from newly developing sensor technologies. Efficacy of the proposed ATD algorithm with the SVM classifier is demonstrated by the Receiver Operating Characteristics (ROC) curve that relates Probability of Detection (PD) as a function of Probability of False Alarm (PFA). The tests performed using CT data of passenger bags shows excellent performance characteristics.

Speckle-learning-based object recognition through scattering media.

PubMed

Ando, Takamasa; Horisaki, Ryoichi; Tanida, Jun

2015-12-28

We experimentally demonstrated object recognition through scattering media based on direct machine learning of a number of speckle intensity images. In the experiments, speckle intensity images of amplitude or phase objects on a spatial light modulator between scattering plates were captured by a camera. We used the support vector machine for binary classification of the captured speckle intensity images of face and non-face data. The experimental results showed that speckles are sufficient for machine learning.

Multiclass Classification for the Differential Diagnosis on the ADHD Subtypes Using Recursive Feature Elimination and Hierarchical Extreme Learning Machine: Structural MRI Study

PubMed Central

Qureshi, Muhammad Naveed Iqbal; Min, Beomjun; Jo, Hang Joon; Lee, Boreom

2016-01-01

The classification of neuroimaging data for the diagnosis of certain brain diseases is one of the main research goals of the neuroscience and clinical communities. In this study, we performed multiclass classification using a hierarchical extreme learning machine (H-ELM) classifier. We compared the performance of this classifier with that of a support vector machine (SVM) and basic extreme learning machine (ELM) for cortical MRI data from attention deficit/hyperactivity disorder (ADHD) patients. We used 159 structural MRI images of children from the publicly available ADHD-200 MRI dataset. The data consisted of three types, namely, typically developing (TDC), ADHD-inattentive (ADHD-I), and ADHD-combined (ADHD-C). We carried out feature selection by using standard SVM-based recursive feature elimination (RFE-SVM) that enabled us to achieve good classification accuracy (60.78%). In this study, we found the RFE-SVM feature selection approach in combination with H-ELM to effectively enable the acquisition of high multiclass classification accuracy rates for structural neuroimaging data. In addition, we found that the most important features for classification were the surface area of the superior frontal lobe, and the cortical thickness, volume, and mean surface area of the whole cortex. PMID:27500640

Multiclass Classification for the Differential Diagnosis on the ADHD Subtypes Using Recursive Feature Elimination and Hierarchical Extreme Learning Machine: Structural MRI Study.

PubMed

Qureshi, Muhammad Naveed Iqbal; Min, Beomjun; Jo, Hang Joon; Lee, Boreom

2016-01-01

The classification of neuroimaging data for the diagnosis of certain brain diseases is one of the main research goals of the neuroscience and clinical communities. In this study, we performed multiclass classification using a hierarchical extreme learning machine (H-ELM) classifier. We compared the performance of this classifier with that of a support vector machine (SVM) and basic extreme learning machine (ELM) for cortical MRI data from attention deficit/hyperactivity disorder (ADHD) patients. We used 159 structural MRI images of children from the publicly available ADHD-200 MRI dataset. The data consisted of three types, namely, typically developing (TDC), ADHD-inattentive (ADHD-I), and ADHD-combined (ADHD-C). We carried out feature selection by using standard SVM-based recursive feature elimination (RFE-SVM) that enabled us to achieve good classification accuracy (60.78%). In this study, we found the RFE-SVM feature selection approach in combination with H-ELM to effectively enable the acquisition of high multiclass classification accuracy rates for structural neuroimaging data. In addition, we found that the most important features for classification were the surface area of the superior frontal lobe, and the cortical thickness, volume, and mean surface area of the whole cortex.

Identifying predictive features in drug response using machine learning: opportunities and challenges.

PubMed

Vidyasagar, Mathukumalli

2015-01-01

This article reviews several techniques from machine learning that can be used to study the problem of identifying a small number of features, from among tens of thousands of measured features, that can accurately predict a drug response. Prediction problems are divided into two categories: sparse classification and sparse regression. In classification, the clinical parameter to be predicted is binary, whereas in regression, the parameter is a real number. Well-known methods for both classes of problems are briefly discussed. These include the SVM (support vector machine) for classification and various algorithms such as ridge regression, LASSO (least absolute shrinkage and selection operator), and EN (elastic net) for regression. In addition, several well-established methods that do not directly fall into machine learning theory are also reviewed, including neural networks, PAM (pattern analysis for microarrays), SAM (significance analysis for microarrays), GSEA (gene set enrichment analysis), and k-means clustering. Several references indicative of the application of these methods to cancer biology are discussed.

Granular support vector machines with association rules mining for protein homology prediction.

PubMed

Tang, Yuchun; Jin, Bo; Zhang, Yan-Qing

2005-01-01

Protein homology prediction between protein sequences is one of critical problems in computational biology. Such a complex classification problem is common in medical or biological information processing applications. How to build a model with superior generalization capability from training samples is an essential issue for mining knowledge to accurately predict/classify unseen new samples and to effectively support human experts to make correct decisions. A new learning model called granular support vector machines (GSVM) is proposed based on our previous work. GSVM systematically and formally combines the principles from statistical learning theory and granular computing theory and thus provides an interesting new mechanism to address complex classification problems. It works by building a sequence of information granules and then building support vector machines (SVM) in some of these information granules on demand. A good granulation method to find suitable granules is crucial for modeling a GSVM with good performance. In this paper, we also propose an association rules-based granulation method. For the granules induced by association rules with high enough confidence and significant support, we leave them as they are because of their high "purity" and significant effect on simplifying the classification task. For every other granule, a SVM is modeled to discriminate the corresponding data. In this way, a complex classification problem is divided into multiple smaller problems so that the learning task is simplified. The proposed algorithm, here named GSVM-AR, is compared with SVM by KDDCUP04 protein homology prediction data. The experimental results show that finding the splitting hyperplane is not a trivial task (we should be careful to select the association rules to avoid overfitting) and GSVM-AR does show significant improvement compared to building one single SVM in the whole feature space. Another advantage is that the utility of GSVM-AR is very good because it is easy to be implemented. More importantly and more interestingly, GSVM provides a new mechanism to address complex classification problems.

Using support vector machines to identify literacy skills: Evidence from eye movements.

PubMed

Lou, Ya; Liu, Yanping; Kaakinen, Johanna K; Li, Xingshan

2017-06-01

Is inferring readers' literacy skills possible by analyzing their eye movements during text reading? This study used Support Vector Machines (SVM) to analyze eye movement data from 61 undergraduate students who read a multiple-paragraph, multiple-topic expository text. Forward fixation time, first-pass rereading time, second-pass fixation time, and regression path reading time on different regions of the text were provided as features. The SVM classification algorithm assisted in distinguishing high-literacy-skilled readers from low-literacy-skilled readers with 80.3 % accuracy. Results demonstrate the effectiveness of combining eye tracking and machine learning techniques to detect readers with low literacy skills, and suggest that such approaches can be potentially used in predicting other cognitive abilities.

Big genomics and clinical data analytics strategies for precision cancer prognosis.

PubMed

Ow, Ghim Siong; Kuznetsov, Vladimir A

2016-11-07

The field of personalized and precise medicine in the era of big data analytics is growing rapidly. Previously, we proposed our model of patient classification termed Prognostic Signature Vector Matching (PSVM) and identified a 37 variable signature comprising 36 let-7b associated prognostic significant mRNAs and the age risk factor that stratified large high-grade serous ovarian cancer patient cohorts into three survival-significant risk groups. Here, we investigated the predictive performance of PSVM via optimization of the prognostic variable weights, which represent the relative importance of one prognostic variable over the others. In addition, we compared several multivariate prognostic models based on PSVM with classical machine learning techniques such as K-nearest-neighbor, support vector machine, random forest, neural networks and logistic regression. Our results revealed that negative log-rank p-values provides more robust weight values as opposed to the use of other quantities such as hazard ratios, fold change, or a combination of those factors. PSVM, together with the classical machine learning classifiers were combined in an ensemble (multi-test) voting system, which collectively provides a more precise and reproducible patient stratification. The use of the multi-test system approach, rather than the search for the ideal classification/prediction method, might help to address limitations of the individual classification algorithm in specific situation.

Extracting Information from Electronic Medical Records to Identify the Obesity Status of a Patient Based on Comorbidities and Bodyweight Measures.

PubMed

Figueroa, Rosa L; Flores, Christopher A

2016-08-01

Obesity is a chronic disease with an increasing impact on the world's population. In this work, we present a method of identifying obesity automatically using text mining techniques and information related to body weight measures and obesity comorbidities. We used a dataset of 3015 de-identified medical records that contain labels for two classification problems. The first classification problem distinguishes between obesity, overweight, normal weight, and underweight. The second classification problem differentiates between obesity types: super obesity, morbid obesity, severe obesity and moderate obesity. We used a Bag of Words approach to represent the records together with unigram and bigram representations of the features. We implemented two approaches: a hierarchical method and a nonhierarchical one. We used Support Vector Machine and Naïve Bayes together with ten-fold cross validation to evaluate and compare performances. Our results indicate that the hierarchical approach does not work as well as the nonhierarchical one. In general, our results show that Support Vector Machine obtains better performances than Naïve Bayes for both classification problems. We also observed that bigram representation improves performance compared with unigram representation.

Machine Learning-based Texture Analysis of Contrast-enhanced MR Imaging to Differentiate between Glioblastoma and Primary Central Nervous System Lymphoma.

PubMed

Kunimatsu, Akira; Kunimatsu, Natsuko; Yasaka, Koichiro; Akai, Hiroyuki; Kamiya, Kouhei; Watadani, Takeyuki; Mori, Harushi; Abe, Osamu

2018-05-16

Although advanced MRI techniques are increasingly available, imaging differentiation between glioblastoma and primary central nervous system lymphoma (PCNSL) is sometimes confusing. We aimed to evaluate the performance of image classification by support vector machine, a method of traditional machine learning, using texture features computed from contrast-enhanced T 1 -weighted images. This retrospective study on preoperative brain tumor MRI included 76 consecutives, initially treated patients with glioblastoma (n = 55) or PCNSL (n = 21) from one institution, consisting of independent training group (n = 60: 44 glioblastomas and 16 PCNSLs) and test group (n = 16: 11 glioblastomas and 5 PCNSLs) sequentially separated by time periods. A total set of 67 texture features was computed on routine contrast-enhanced T 1 -weighted images of the training group, and the top four most discriminating features were selected as input variables to train support vector machine classifiers. These features were then evaluated on the test group with subsequent image classification. The area under the receiver operating characteristic curves on the training data was calculated at 0.99 (95% confidence interval [CI]: 0.96-1.00) for the classifier with a Gaussian kernel and 0.87 (95% CI: 0.77-0.95) for the classifier with a linear kernel. On the test data, both of the classifiers showed prediction accuracy of 75% (12/16) of the test images. Although further improvement is needed, our preliminary results suggest that machine learning-based image classification may provide complementary diagnostic information on routine brain MRI.

Evaluating the statistical performance of less applied algorithms in classification of worldview-3 imagery data in an urbanized landscape

NASA Astrophysics Data System (ADS)

Ranaie, Mehrdad; Soffianian, Alireza; Pourmanafi, Saeid; Mirghaffari, Noorollah; Tarkesh, Mostafa

2018-03-01

In recent decade, analyzing the remotely sensed imagery is considered as one of the most common and widely used procedures in the environmental studies. In this case, supervised image classification techniques play a central role. Hence, taking a high resolution Worldview-3 over a mixed urbanized landscape in Iran, three less applied image classification methods including Bagged CART, Stochastic gradient boosting model and Neural network with feature extraction were tested and compared with two prevalent methods: random forest and support vector machine with linear kernel. To do so, each method was run ten time and three validation techniques was used to estimate the accuracy statistics consist of cross validation, independent validation and validation with total of train data. Moreover, using ANOVA and Tukey test, statistical difference significance between the classification methods was significantly surveyed. In general, the results showed that random forest with marginal difference compared to Bagged CART and stochastic gradient boosting model is the best performing method whilst based on independent validation there was no significant difference between the performances of classification methods. It should be finally noted that neural network with feature extraction and linear support vector machine had better processing speed than other.

Assessing the performance of multiple spectral-spatial features of a hyperspectral image for classification of urban land cover classes using support vector machines and artificial neural network

NASA Astrophysics Data System (ADS)

Pullanagari, Reddy; Kereszturi, Gábor; Yule, Ian J.; Ghamisi, Pedram

2017-04-01

Accurate and spatially detailed mapping of complex urban environments is essential for land managers. Classifying high spectral and spatial resolution hyperspectral images is a challenging task because of its data abundance and computational complexity. Approaches with a combination of spectral and spatial information in a single classification framework have attracted special attention because of their potential to improve the classification accuracy. We extracted multiple features from spectral and spatial domains of hyperspectral images and evaluated them with two supervised classification algorithms; support vector machines (SVM) and an artificial neural network. The spatial features considered are produced by a gray level co-occurrence matrix and extended multiattribute profiles. All of these features were stacked, and the most informative features were selected using a genetic algorithm-based SVM. After selecting the most informative features, the classification model was integrated with a segmentation map derived using a hidden Markov random field. We tested the proposed method on a real application of a hyperspectral image acquired from AisaFENIX and on widely used hyperspectral images. From the results, it can be concluded that the proposed framework significantly improves the results with different spectral and spatial resolutions over different instrumentation.

Towards human behavior recognition based on spatio temporal features and support vector machines

NASA Astrophysics Data System (ADS)

Ghabri, Sawsen; Ouarda, Wael; Alimi, Adel M.

2017-03-01

Security and surveillance are vital issues in today's world. The recent acts of terrorism have highlighted the urgent need for efficient surveillance. There is indeed a need for an automated system for video surveillance which can detect identity and activity of person. In this article, we propose a new paradigm to recognize an aggressive human behavior such as boxing action. Our proposed system for human activity detection includes the use of a fusion between Spatio Temporal Interest Point (STIP) and Histogram of Oriented Gradient (HoG) features. The novel feature called Spatio Temporal Histogram Oriented Gradient (STHOG). To evaluate the robustness of our proposed paradigm with a local application of HoG technique on STIP points, we made experiments on KTH human action dataset based on Multi Class Support Vector Machines classification. The proposed scheme outperforms basic descriptors like HoG and STIP to achieve 82.26% us an accuracy value of classification rate.

Unresolved Galaxy Classifier for ESA/Gaia mission: Support Vector Machines approach

NASA Astrophysics Data System (ADS)

Bellas-Velidis, Ioannis; Kontizas, Mary; Dapergolas, Anastasios; Livanou, Evdokia; Kontizas, Evangelos; Karampelas, Antonios

A software package Unresolved Galaxy Classifier (UGC) is being developed for the ground-based pipeline of ESA's Gaia mission. It aims to provide an automated taxonomic classification and specific parameters estimation analyzing Gaia BP/RP instrument low-dispersion spectra of unresolved galaxies. The UGC algorithm is based on a supervised learning technique, the Support Vector Machines (SVM). The software is implemented in Java as two separate modules. An offline learning module provides functions for SVM-models training. Once trained, the set of models can be repeatedly applied to unknown galaxy spectra by the pipeline's application module. A library of galaxy models synthetic spectra, simulated for the BP/RP instrument, is used to train and test the modules. Science tests show a very good classification performance of UGC and relatively good regression performance, except for some of the parameters. Possible approaches to improve the performance are discussed.

A Hybrid Approach of Stepwise Regression, Logistic Regression, Support Vector Machine, and Decision Tree for Forecasting Fraudulent Financial Statements

PubMed Central

Goo, Yeong-Jia James; Shen, Zone-De

2014-01-01

As the fraudulent financial statement of an enterprise is increasingly serious with each passing day, establishing a valid forecasting fraudulent financial statement model of an enterprise has become an important question for academic research and financial practice. After screening the important variables using the stepwise regression, the study also matches the logistic regression, support vector machine, and decision tree to construct the classification models to make a comparison. The study adopts financial and nonfinancial variables to assist in establishment of the forecasting fraudulent financial statement model. Research objects are the companies to which the fraudulent and nonfraudulent financial statement happened between years 1998 to 2012. The findings are that financial and nonfinancial information are effectively used to distinguish the fraudulent financial statement, and decision tree C5.0 has the best classification effect 85.71%. PMID:25302338

Acoustic Biometric System Based on Preprocessing Techniques and Linear Support Vector Machines

PubMed Central

del Val, Lara; Izquierdo-Fuente, Alberto; Villacorta, Juan J.; Raboso, Mariano

2015-01-01

Drawing on the results of an acoustic biometric system based on a MSE classifier, a new biometric system has been implemented. This new system preprocesses acoustic images, extracts several parameters and finally classifies them, based on Support Vector Machine (SVM). The preprocessing techniques used are spatial filtering, segmentation—based on a Gaussian Mixture Model (GMM) to separate the person from the background, masking—to reduce the dimensions of images—and binarization—to reduce the size of each image. An analysis of classification error and a study of the sensitivity of the error versus the computational burden of each implemented algorithm are presented. This allows the selection of the most relevant algorithms, according to the benefits required by the system. A significant improvement of the biometric system has been achieved by reducing the classification error, the computational burden and the storage requirements. PMID:26091392

A hybrid approach of stepwise regression, logistic regression, support vector machine, and decision tree for forecasting fraudulent financial statements.

PubMed

Chen, Suduan; Goo, Yeong-Jia James; Shen, Zone-De

2014-01-01

As the fraudulent financial statement of an enterprise is increasingly serious with each passing day, establishing a valid forecasting fraudulent financial statement model of an enterprise has become an important question for academic research and financial practice. After screening the important variables using the stepwise regression, the study also matches the logistic regression, support vector machine, and decision tree to construct the classification models to make a comparison. The study adopts financial and nonfinancial variables to assist in establishment of the forecasting fraudulent financial statement model. Research objects are the companies to which the fraudulent and nonfraudulent financial statement happened between years 1998 to 2012. The findings are that financial and nonfinancial information are effectively used to distinguish the fraudulent financial statement, and decision tree C5.0 has the best classification effect 85.71%.

Acoustic Biometric System Based on Preprocessing Techniques and Linear Support Vector Machines.

PubMed

del Val, Lara; Izquierdo-Fuente, Alberto; Villacorta, Juan J; Raboso, Mariano

2015-06-17

Drawing on the results of an acoustic biometric system based on a MSE classifier, a new biometric system has been implemented. This new system preprocesses acoustic images, extracts several parameters and finally classifies them, based on Support Vector Machine (SVM). The preprocessing techniques used are spatial filtering, segmentation-based on a Gaussian Mixture Model (GMM) to separate the person from the background, masking-to reduce the dimensions of images-and binarization-to reduce the size of each image. An analysis of classification error and a study of the sensitivity of the error versus the computational burden of each implemented algorithm are presented. This allows the selection of the most relevant algorithms, according to the benefits required by the system. A significant improvement of the biometric system has been achieved by reducing the classification error, the computational burden and the storage requirements.

A comparison of graph- and kernel-based -omics data integration algorithms for classifying complex traits.

PubMed

Yan, Kang K; Zhao, Hongyu; Pang, Herbert

2017-12-06

High-throughput sequencing data are widely collected and analyzed in the study of complex diseases in quest of improving human health. Well-studied algorithms mostly deal with single data source, and cannot fully utilize the potential of these multi-omics data sources. In order to provide a holistic understanding of human health and diseases, it is necessary to integrate multiple data sources. Several algorithms have been proposed so far, however, a comprehensive comparison of data integration algorithms for classification of binary traits is currently lacking. In this paper, we focus on two common classes of integration algorithms, graph-based that depict relationships with subjects denoted by nodes and relationships denoted by edges, and kernel-based that can generate a classifier in feature space. Our paper provides a comprehensive comparison of their performance in terms of various measurements of classification accuracy and computation time. Seven different integration algorithms, including graph-based semi-supervised learning, graph sharpening integration, composite association network, Bayesian network, semi-definite programming-support vector machine (SDP-SVM), relevance vector machine (RVM) and Ada-boost relevance vector machine are compared and evaluated with hypertension and two cancer data sets in our study. In general, kernel-based algorithms create more complex models and require longer computation time, but they tend to perform better than graph-based algorithms. The performance of graph-based algorithms has the advantage of being faster computationally. The empirical results demonstrate that composite association network, relevance vector machine, and Ada-boost RVM are the better performers. We provide recommendations on how to choose an appropriate algorithm for integrating data from multiple sources.

A multiple kernel support vector machine scheme for feature selection and rule extraction from gene expression data of cancer tissue.

PubMed

Chen, Zhenyu; Li, Jianping; Wei, Liwei

2007-10-01

Recently, gene expression profiling using microarray techniques has been shown as a promising tool to improve the diagnosis and treatment of cancer. Gene expression data contain high level of noise and the overwhelming number of genes relative to the number of available samples. It brings out a great challenge for machine learning and statistic techniques. Support vector machine (SVM) has been successfully used to classify gene expression data of cancer tissue. In the medical field, it is crucial to deliver the user a transparent decision process. How to explain the computed solutions and present the extracted knowledge becomes a main obstacle for SVM. A multiple kernel support vector machine (MK-SVM) scheme, consisting of feature selection, rule extraction and prediction modeling is proposed to improve the explanation capacity of SVM. In this scheme, we show that the feature selection problem can be translated into an ordinary multiple parameters learning problem. And a shrinkage approach: 1-norm based linear programming is proposed to obtain the sparse parameters and the corresponding selected features. We propose a novel rule extraction approach using the information provided by the separating hyperplane and support vectors to improve the generalization capacity and comprehensibility of rules and reduce the computational complexity. Two public gene expression datasets: leukemia dataset and colon tumor dataset are used to demonstrate the performance of this approach. Using the small number of selected genes, MK-SVM achieves encouraging classification accuracy: more than 90% for both two datasets. Moreover, very simple rules with linguist labels are extracted. The rule sets have high diagnostic power because of their good classification performance.

High-order distance-based multiview stochastic learning in image classification.

PubMed

Yu, Jun; Rui, Yong; Tang, Yuan Yan; Tao, Dacheng

2014-12-01

How do we find all images in a larger set of images which have a specific content? Or estimate the position of a specific object relative to the camera? Image classification methods, like support vector machine (supervised) and transductive support vector machine (semi-supervised), are invaluable tools for the applications of content-based image retrieval, pose estimation, and optical character recognition. However, these methods only can handle the images represented by single feature. In many cases, different features (or multiview data) can be obtained, and how to efficiently utilize them is a challenge. It is inappropriate for the traditionally concatenating schema to link features of different views into a long vector. The reason is each view has its specific statistical property and physical interpretation. In this paper, we propose a high-order distance-based multiview stochastic learning (HD-MSL) method for image classification. HD-MSL effectively combines varied features into a unified representation and integrates the labeling information based on a probabilistic framework. In comparison with the existing strategies, our approach adopts the high-order distance obtained from the hypergraph to replace pairwise distance in estimating the probability matrix of data distribution. In addition, the proposed approach can automatically learn a combination coefficient for each view, which plays an important role in utilizing the complementary information of multiview data. An alternative optimization is designed to solve the objective functions of HD-MSL and obtain different views on coefficients and classification scores simultaneously. Experiments on two real world datasets demonstrate the effectiveness of HD-MSL in image classification.

Semantic classification of business images

NASA Astrophysics Data System (ADS)

Erol, Berna; Hull, Jonathan J.

2006-01-01

Digital cameras are becoming increasingly common for capturing information in business settings. In this paper, we describe a novel method for classifying images into the following semantic classes: document, whiteboard, business card, slide, and regular images. Our method is based on combining low-level image features, such as text color, layout, and handwriting features with high-level OCR output analysis. Several Support Vector Machine Classifiers are combined for multi-class classification of input images. The system yields 95% accuracy in classification.

Adiabatic Quantum Anomaly Detection and Machine Learning

NASA Astrophysics Data System (ADS)

Pudenz, Kristen; Lidar, Daniel

2012-02-01

We present methods of anomaly detection and machine learning using adiabatic quantum computing. The machine learning algorithm is a boosting approach which seeks to optimally combine somewhat accurate classification functions to create a unified classifier which is much more accurate than its components. This algorithm then becomes the first part of the larger anomaly detection algorithm. In the anomaly detection routine, we first use adiabatic quantum computing to train two classifiers which detect two sets, the overlap of which forms the anomaly class. We call this the learning phase. Then, in the testing phase, the two learned classification functions are combined to form the final Hamiltonian for an adiabatic quantum computation, the low energy states of which represent the anomalies in a binary vector space.

Antepartum fetal heart rate feature extraction and classification using empirical mode decomposition and support vector machine

PubMed Central

2011-01-01

Background Cardiotocography (CTG) is the most widely used tool for fetal surveillance. The visual analysis of fetal heart rate (FHR) traces largely depends on the expertise and experience of the clinician involved. Several approaches have been proposed for the effective interpretation of FHR. In this paper, a new approach for FHR feature extraction based on empirical mode decomposition (EMD) is proposed, which was used along with support vector machine (SVM) for the classification of FHR recordings as 'normal' or 'at risk'. Methods The FHR were recorded from 15 subjects at a sampling rate of 4 Hz and a dataset consisting of 90 randomly selected records of 20 minutes duration was formed from these. All records were labelled as 'normal' or 'at risk' by two experienced obstetricians. A training set was formed by 60 records, the remaining 30 left as the testing set. The standard deviations of the EMD components are input as features to a support vector machine (SVM) to classify FHR samples. Results For the training set, a five-fold cross validation test resulted in an accuracy of 86% whereas the overall geometric mean of sensitivity and specificity was 94.8%. The Kappa value for the training set was .923. Application of the proposed method to the testing set (30 records) resulted in a geometric mean of 81.5%. The Kappa value for the testing set was .684. Conclusions Based on the overall performance of the system it can be stated that the proposed methodology is a promising new approach for the feature extraction and classification of FHR signals. PMID:21244712

Detection of Pathological Voice Using Cepstrum Vectors: A Deep Learning Approach.

PubMed

Fang, Shih-Hau; Tsao, Yu; Hsiao, Min-Jing; Chen, Ji-Ying; Lai, Ying-Hui; Lin, Feng-Chuan; Wang, Chi-Te

2018-03-19

Computerized detection of voice disorders has attracted considerable academic and clinical interest in the hope of providing an effective screening method for voice diseases before endoscopic confirmation. This study proposes a deep-learning-based approach to detect pathological voice and examines its performance and utility compared with other automatic classification algorithms. This study retrospectively collected 60 normal voice samples and 402 pathological voice samples of 8 common clinical voice disorders in a voice clinic of a tertiary teaching hospital. We extracted Mel frequency cepstral coefficients from 3-second samples of a sustained vowel. The performances of three machine learning algorithms, namely, deep neural network (DNN), support vector machine, and Gaussian mixture model, were evaluated based on a fivefold cross-validation. Collective cases from the voice disorder database of MEEI (Massachusetts Eye and Ear Infirmary) were used to verify the performance of the classification mechanisms. The experimental results demonstrated that DNN outperforms Gaussian mixture model and support vector machine. Its accuracy in detecting voice pathologies reached 94.26% and 90.52% in male and female subjects, based on three representative Mel frequency cepstral coefficient features. When applied to the MEEI database for validation, the DNN also achieved a higher accuracy (99.32%) than the other two classification algorithms. By stacking several layers of neurons with optimized weights, the proposed DNN algorithm can fully utilize the acoustic features and efficiently differentiate between normal and pathological voice samples. Based on this pilot study, future research may proceed to explore more application of DNN from laboratory and clinical perspectives. Copyright © 2018 The Voice Foundation. Published by Elsevier Inc. All rights reserved.

Multi-phase classification by a least-squares support vector machine approach in tomography images of geological samples

NASA Astrophysics Data System (ADS)

Khan, Faisal; Enzmann, Frieder; Kersten, Michael

2016-03-01

Image processing of X-ray-computed polychromatic cone-beam micro-tomography (μXCT) data of geological samples mainly involves artefact reduction and phase segmentation. For the former, the main beam-hardening (BH) artefact is removed by applying a best-fit quadratic surface algorithm to a given image data set (reconstructed slice), which minimizes the BH offsets of the attenuation data points from that surface. A Matlab code for this approach is provided in the Appendix. The final BH-corrected image is extracted from the residual data or from the difference between the surface elevation values and the original grey-scale values. For the segmentation, we propose a novel least-squares support vector machine (LS-SVM, an algorithm for pixel-based multi-phase classification) approach. A receiver operating characteristic (ROC) analysis was performed on BH-corrected and uncorrected samples to show that BH correction is in fact an important prerequisite for accurate multi-phase classification. The combination of the two approaches was thus used to classify successfully three different more or less complex multi-phase rock core samples.

Hybrid three-dimensional and support vector machine approach for automatic vehicle tracking and classification using a single camera

NASA Astrophysics Data System (ADS)

Kachach, Redouane; Cañas, José María

2016-05-01

Using video in traffic monitoring is one of the most active research domains in the computer vision community. TrafficMonitor, a system that employs a hybrid approach for automatic vehicle tracking and classification on highways using a simple stationary calibrated camera, is presented. The proposed system consists of three modules: vehicle detection, vehicle tracking, and vehicle classification. Moving vehicles are detected by an enhanced Gaussian mixture model background estimation algorithm. The design includes a technique to resolve the occlusion problem by using a combination of two-dimensional proximity tracking algorithm and the Kanade-Lucas-Tomasi feature tracking algorithm. The last module classifies the shapes identified into five vehicle categories: motorcycle, car, van, bus, and truck by using three-dimensional templates and an algorithm based on histogram of oriented gradients and the support vector machine classifier. Several experiments have been performed using both real and simulated traffic in order to validate the system. The experiments were conducted on GRAM-RTM dataset and a proper real video dataset which is made publicly available as part of this work.

Identification and Mapping of Tree Species in Urban Areas Using WORLDVIEW-2 Imagery

NASA Astrophysics Data System (ADS)

Mustafa, Y. T.; Habeeb, H. N.; Stein, A.; Sulaiman, F. Y.

2015-10-01

Monitoring and mapping of urban trees are essential to provide urban forestry authorities with timely and consistent information. Modern techniques increasingly facilitate these tasks, but require the development of semi-automatic tree detection and classification methods. In this article, we propose an approach to delineate and map the crown of 15 tree species in the city of Duhok, Kurdistan Region of Iraq using WorldView-2 (WV-2) imagery. A tree crown object is identified first and is subsequently delineated as an image object (IO) using vegetation indices and texture measurements. Next, three classification methods: Maximum Likelihood, Neural Network, and Support Vector Machine were used to classify IOs using selected IO features. The best results are obtained with Support Vector Machine classification that gives the best map of urban tree species in Duhok. The overall accuracy was between 60.93% to 88.92% and κ-coefficient was between 0.57 to 0.75. We conclude that fifteen tree species were identified and mapped at a satisfactory accuracy in urban areas of this study.

Symbolic rule-based classification of lung cancer stages from free-text pathology reports.

PubMed

Nguyen, Anthony N; Lawley, Michael J; Hansen, David P; Bowman, Rayleen V; Clarke, Belinda E; Duhig, Edwina E; Colquist, Shoni

2010-01-01

To classify automatically lung tumor-node-metastases (TNM) cancer stages from free-text pathology reports using symbolic rule-based classification. By exploiting report substructure and the symbolic manipulation of systematized nomenclature of medicine-clinical terms (SNOMED CT) concepts in reports, statements in free text can be evaluated for relevance against factors relating to the staging guidelines. Post-coordinated SNOMED CT expressions based on templates were defined and populated by concepts in reports, and tested for subsumption by staging factors. The subsumption results were used to build logic according to the staging guidelines to calculate the TNM stage. The accuracy measure and confusion matrices were used to evaluate the TNM stages classified by the symbolic rule-based system. The system was evaluated against a database of multidisciplinary team staging decisions and a machine learning-based text classification system using support vector machines. Overall accuracy on a corpus of pathology reports for 718 lung cancer patients against a database of pathological TNM staging decisions were 72%, 78%, and 94% for T, N, and M staging, respectively. The system's performance was also comparable to support vector machine classification approaches. A system to classify lung TNM stages from free-text pathology reports was developed, and it was verified that the symbolic rule-based approach using SNOMED CT can be used for the extraction of key lung cancer characteristics from free-text reports. Future work will investigate the applicability of using the proposed methodology for extracting other cancer characteristics and types.

Support-vector-machines-based multidimensional signal classification for fetal activity characterization

NASA Astrophysics Data System (ADS)

Ribes, S.; Voicu, I.; Girault, J. M.; Fournier, M.; Perrotin, F.; Tranquart, F.; Kouamé, D.

2011-03-01

Electronic fetal monitoring may be required during the whole pregnancy to closely monitor specific fetal and maternal disorders. Currently used methods suffer from many limitations and are not sufficient to evaluate fetal asphyxia. Fetal activity parameters such as movements, heart rate and associated parameters are essential indicators of the fetus well being, and no current device gives a simultaneous and sufficient estimation of all these parameters to evaluate the fetus well-being. We built for this purpose, a multi-transducer-multi-gate Doppler system and developed dedicated signal processing techniques for fetal activity parameter extraction in order to investigate fetus's asphyxia or well-being through fetal activity parameters. To reach this goal, this paper shows preliminary feasibility of separating normal and compromised fetuses using our system. To do so, data set consisting of two groups of fetal signals (normal and compromised) has been established and provided by physicians. From estimated parameters an instantaneous Manning-like score, referred to as ultrasonic score was introduced and was used together with movements, heart rate and associated parameters in a classification process using Support Vector Machines (SVM) method. The influence of the fetal activity parameters and the performance of the SVM were evaluated using the computation of sensibility, specificity, percentage of support vectors and total classification accuracy. We showed our ability to separate the data into two sets : normal fetuses and compromised fetuses and obtained an excellent matching with the clinical classification performed by physician.

Structural brain changes versus self-report: machine-learning classification of chronic fatigue syndrome patients.

PubMed

Sevel, Landrew S; Boissoneault, Jeff; Letzen, Janelle E; Robinson, Michael E; Staud, Roland

2018-05-30

Chronic fatigue syndrome (CFS) is a disorder associated with fatigue, pain, and structural/functional abnormalities seen during magnetic resonance brain imaging (MRI). Therefore, we evaluated the performance of structural MRI (sMRI) abnormalities in the classification of CFS patients versus healthy controls and compared it to machine learning (ML) classification based upon self-report (SR). Participants included 18 CFS patients and 15 healthy controls (HC). All subjects underwent T1-weighted sMRI and provided visual analogue-scale ratings of fatigue, pain intensity, anxiety, depression, anger, and sleep quality. sMRI data were segmented using FreeSurfer and 61 regions based on functional and structural abnormalities previously reported in patients with CFS. Classification was performed in RapidMiner using a linear support vector machine and bootstrap optimism correction. We compared ML classifiers based on (1) 61 a priori sMRI regional estimates and (2) SR ratings. The sMRI model achieved 79.58% classification accuracy. The SR (accuracy = 95.95%) outperformed both sMRI models. Estimates from multiple brain areas related to cognition, emotion, and memory contributed strongly to group classification. This is the first ML-based group classification of CFS. Our findings suggest that sMRI abnormalities are useful for discriminating CFS patients from HC, but SR ratings remain most effective in classification tasks.

a Gsa-Svm Hybrid System for Classification of Binary Problems

NASA Astrophysics Data System (ADS)

Sarafrazi, Soroor; Nezamabadi-pour, Hossein; Barahman, Mojgan

2011-06-01

This paperhybridizesgravitational search algorithm (GSA) with support vector machine (SVM) and made a novel GSA-SVM hybrid system to improve the classification accuracy in binary problems. GSA is an optimization heuristic toolused to optimize the value of SVM kernel parameter (in this paper, radial basis function (RBF) is chosen as the kernel function). The experimental results show that this newapproach can achieve high classification accuracy and is comparable to or better than the particle swarm optimization (PSO)-SVM and genetic algorithm (GA)-SVM, which are two hybrid systems for classification.

Classification of change detection and change blindness from near-infrared spectroscopy signals

NASA Astrophysics Data System (ADS)

Tanaka, Hirokazu; Katura, Takusige

2011-08-01

Using a machine-learning classification algorithm applied to near-infrared spectroscopy (NIRS) signals, we classify a success (change detection) or a failure (change blindness) in detecting visual changes for a change-detection task. Five subjects perform a change-detection task, and their brain activities are continuously monitored. A support-vector-machine algorithm is applied to classify the change-detection and change-blindness trials, and correct classification probability of 70-90% is obtained for four subjects. Two types of temporal shapes in classification probabilities are found: one exhibiting a maximum value after the task is completed (postdictive type), and another exhibiting a maximum value during the task (predictive type). As for the postdictive type, the classification probability begins to increase immediately after the task completion and reaches its maximum in about the time scale of neuronal hemodynamic response, reflecting a subjective report of change detection. As for the predictive type, the classification probability shows an increase at the task initiation and is maximal while subjects are performing the task, predicting the task performance in detecting a change. We conclude that decoding change detection and change blindness from NIRS signal is possible and argue some future applications toward brain-machine interfaces.

Agent Collaborative Target Localization and Classification in Wireless Sensor Networks

PubMed Central

Wang, Xue; Bi, Dao-wei; Ding, Liang; Wang, Sheng

2007-01-01

Wireless sensor networks (WSNs) are autonomous networks that have been frequently deployed to collaboratively perform target localization and classification tasks. Their autonomous and collaborative features resemble the characteristics of agents. Such similarities inspire the development of heterogeneous agent architecture for WSN in this paper. The proposed agent architecture views WSN as multi-agent systems and mobile agents are employed to reduce in-network communication. According to the architecture, an energy based acoustic localization algorithm is proposed. In localization, estimate of target location is obtained by steepest descent search. The search algorithm adapts to measurement environments by dynamically adjusting its termination condition. With the agent architecture, target classification is accomplished by distributed support vector machine (SVM). Mobile agents are employed for feature extraction and distributed SVM learning to reduce communication load. Desirable learning performance is guaranteed by combining support vectors and convex hull vectors. Fusion algorithms are designed to merge SVM classification decisions made from various modalities. Real world experiments with MICAz sensor nodes are conducted for vehicle localization and classification. Experimental results show the proposed agent architecture remarkably facilitates WSN designs and algorithm implementation. The localization and classification algorithms also prove to be accurate and energy efficient.

Photometric Supernova Classification with Machine Learning

NASA Astrophysics Data System (ADS)

Lochner, Michelle; McEwen, Jason D.; Peiris, Hiranya V.; Lahav, Ofer; Winter, Max K.

2016-08-01

Automated photometric supernova classification has become an active area of research in recent years in light of current and upcoming imaging surveys such as the Dark Energy Survey (DES) and the Large Synoptic Survey Telescope, given that spectroscopic confirmation of type for all supernovae discovered will be impossible. Here, we develop a multi-faceted classification pipeline, combining existing and new approaches. Our pipeline consists of two stages: extracting descriptive features from the light curves and classification using a machine learning algorithm. Our feature extraction methods vary from model-dependent techniques, namely SALT2 fits, to more independent techniques that fit parametric models to curves, to a completely model-independent wavelet approach. We cover a range of representative machine learning algorithms, including naive Bayes, k-nearest neighbors, support vector machines, artificial neural networks, and boosted decision trees (BDTs). We test the pipeline on simulated multi-band DES light curves from the Supernova Photometric Classification Challenge. Using the commonly used area under the curve (AUC) of the Receiver Operating Characteristic as a metric, we find that the SALT2 fits and the wavelet approach, with the BDTs algorithm, each achieve an AUC of 0.98, where 1 represents perfect classification. We find that a representative training set is essential for good classification, whatever the feature set or algorithm, with implications for spectroscopic follow-up. Importantly, we find that by using either the SALT2 or the wavelet feature sets with a BDT algorithm, accurate classification is possible purely from light curve data, without the need for any redshift information.

Evaluation of different classification methods for the diagnosis of schizophrenia based on functional near-infrared spectroscopy.

PubMed

Li, Zhaohua; Wang, Yuduo; Quan, Wenxiang; Wu, Tongning; Lv, Bin

2015-02-15

Based on near-infrared spectroscopy (NIRS), recent converging evidence has been observed that patients with schizophrenia exhibit abnormal functional activities in the prefrontal cortex during a verbal fluency task (VFT). Therefore, some studies have attempted to employ NIRS measurements to differentiate schizophrenia patients from healthy controls with different classification methods. However, no systematic evaluation was conducted to compare their respective classification performances on the same study population. In this study, we evaluated the classification performance of four classification methods (including linear discriminant analysis, k-nearest neighbors, Gaussian process classifier, and support vector machines) on an NIRS-aided schizophrenia diagnosis. We recruited a large sample of 120 schizophrenia patients and 120 healthy controls and measured the hemoglobin response in the prefrontal cortex during the VFT using a multichannel NIRS system. Features for classification were extracted from three types of NIRS data in each channel. We subsequently performed a principal component analysis (PCA) for feature selection prior to comparison of the different classification methods. We achieved a maximum accuracy of 85.83% and an overall mean accuracy of 83.37% using a PCA-based feature selection on oxygenated hemoglobin signals and support vector machine classifier. This is the first comprehensive evaluation of different classification methods for the diagnosis of schizophrenia based on different types of NIRS signals. Our results suggested that, using the appropriate classification method, NIRS has the potential capacity to be an effective objective biomarker for the diagnosis of schizophrenia. Copyright © 2014 Elsevier B.V. All rights reserved.

Classification of burn wounds using support vector machines

NASA Astrophysics Data System (ADS)

Acha, Begona; Serrano, Carmen; Palencia, Sergio; Murillo, Juan Jose

2004-05-01

The purpose of this work is to improve a previous method developed by the authors for the classification of burn wounds into their depths. The inputs of the system are color and texture information, as these are the characteristics observed by physicians in order to give a diagnosis. Our previous work consisted in segmenting the burn wound from the rest of the image and classifying the burn into its depth. In this paper we focus on the classification problem only. We already proposed to use a Fuzzy-ARTMAP neural network (NN). However, we may take advantage of new powerful classification tools such as Support Vector Machines (SVM). We apply the five-folded cross validation scheme to divide the database into training and validating sets. Then, we apply a feature selection method for each classifier, which will give us the set of features that yields the smallest classification error for each classifier. Features used to classify are first-order statistical parameters extracted from the L*, u* and v* color components of the image. The feature selection algorithms used are the Sequential Forward Selection (SFS) and the Sequential Backward Selection (SBS) methods. As data of the problem faced here are not linearly separable, the SVM was trained using some different kernels. The validating process shows that the SVM method, when using a Gaussian kernel of variance 1, outperforms classification results obtained with the rest of the classifiers, yielding an error classification rate of 0.7% whereas the Fuzzy-ARTMAP NN attained 1.6 %.

Feature selection for elderly faller classification based on wearable sensors.

PubMed

Howcroft, Jennifer; Kofman, Jonathan; Lemaire, Edward D

2017-05-30

Wearable sensors can be used to derive numerous gait pattern features for elderly fall risk and faller classification; however, an appropriate feature set is required to avoid high computational costs and the inclusion of irrelevant features. The objectives of this study were to identify and evaluate smaller feature sets for faller classification from large feature sets derived from wearable accelerometer and pressure-sensing insole gait data. A convenience sample of 100 older adults (75.5 ± 6.7 years; 76 non-fallers, 24 fallers based on 6 month retrospective fall occurrence) walked 7.62 m while wearing pressure-sensing insoles and tri-axial accelerometers at the head, pelvis, left and right shanks. Feature selection was performed using correlation-based feature selection (CFS), fast correlation based filter (FCBF), and Relief-F algorithms. Faller classification was performed using multi-layer perceptron neural network, naïve Bayesian, and support vector machine classifiers, with 75:25 single stratified holdout and repeated random sampling. The best performing model was a support vector machine with 78% accuracy, 26% sensitivity, 95% specificity, 0.36 F1 score, and 0.31 MCC and one posterior pelvis accelerometer input feature (left acceleration standard deviation). The second best model achieved better sensitivity (44%) and used a support vector machine with 74% accuracy, 83% specificity, 0.44 F1 score, and 0.29 MCC. This model had ten input features: maximum, mean and standard deviation posterior acceleration; maximum, mean and standard deviation anterior acceleration; mean superior acceleration; and three impulse features. The best multi-sensor model sensitivity (56%) was achieved using posterior pelvis and both shank accelerometers and a naïve Bayesian classifier. The best single-sensor model sensitivity (41%) was achieved using the posterior pelvis accelerometer and a naïve Bayesian classifier. Feature selection provided models with smaller feature sets and improved faller classification compared to faller classification without feature selection. CFS and FCBF provided the best feature subset (one posterior pelvis accelerometer feature) for faller classification. However, better sensitivity was achieved by the second best model based on a Relief-F feature subset with three pressure-sensing insole features and seven head accelerometer features. Feature selection should be considered as an important step in faller classification using wearable sensors.

Objective research of auscultation signals in Traditional Chinese Medicine based on wavelet packet energy and support vector machine.

PubMed

Yan, Jianjun; Shen, Xiaojing; Wang, Yiqin; Li, Fufeng; Xia, Chunming; Guo, Rui; Chen, Chunfeng; Shen, Qingwei

2010-01-01

This study aims at utilising Wavelet Packet Transform (WPT) and Support Vector Machine (SVM) algorithm to make objective analysis and quantitative research for the auscultation in Traditional Chinese Medicine (TCM) diagnosis. First, Wavelet Packet Decomposition (WPD) at level 6 was employed to split more elaborate frequency bands of the auscultation signals. Then statistic analysis was made based on the extracted Wavelet Packet Energy (WPE) features from WPD coefficients. Furthermore, the pattern recognition was used to distinguish mixed subjects' statistical feature values of sample groups through SVM. Finally, the experimental results showed that the classification accuracies were at a high level.

Identification of handwriting by using the genetic algorithm (GA) and support vector machine (SVM)

NASA Astrophysics Data System (ADS)

Zhang, Qigui; Deng, Kai

2016-12-01

As portable digital camera and a camera phone comes more and more popular, and equally pressing is meeting the requirements of people to shoot at any time, to identify and storage handwritten character. In this paper, genetic algorithm(GA) and support vector machine(SVM)are used for identification of handwriting. Compare with parameters-optimized method, this technique overcomes two defects: first, it's easy to trap in the local optimum; second, finding the best parameters in the larger range will affects the efficiency of classification and prediction. As the experimental results suggest, GA-SVM has a higher recognition rate.

Application of Machine Learning Approaches for Classifying Sitting Posture Based on Force and Acceleration Sensors.

PubMed

Zemp, Roland; Tanadini, Matteo; Plüss, Stefan; Schnüriger, Karin; Singh, Navrag B; Taylor, William R; Lorenzetti, Silvio

2016-01-01

Occupational musculoskeletal disorders, particularly chronic low back pain (LBP), are ubiquitous due to prolonged static sitting or nonergonomic sitting positions. Therefore, the aim of this study was to develop an instrumented chair with force and acceleration sensors to determine the accuracy of automatically identifying the user's sitting position by applying five different machine learning methods (Support Vector Machines, Multinomial Regression, Boosting, Neural Networks, and Random Forest). Forty-one subjects were requested to sit four times in seven different prescribed sitting positions (total 1148 samples). Sixteen force sensor values and the backrest angle were used as the explanatory variables (features) for the classification. The different classification methods were compared by means of a Leave-One-Out cross-validation approach. The best performance was achieved using the Random Forest classification algorithm, producing a mean classification accuracy of 90.9% for subjects with which the algorithm was not familiar. The classification accuracy varied between 81% and 98% for the seven different sitting positions. The present study showed the possibility of accurately classifying different sitting positions by means of the introduced instrumented office chair combined with machine learning analyses. The use of such novel approaches for the accurate assessment of chair usage could offer insights into the relationships between sitting position, sitting behaviour, and the occurrence of musculoskeletal disorders.

Extended robust support vector machine based on financial risk minimization.

PubMed

Takeda, Akiko; Fujiwara, Shuhei; Kanamori, Takafumi

2014-11-01

Financial risk measures have been used recently in machine learning. For example, ν-support vector machine ν-SVM) minimizes the conditional value at risk (CVaR) of margin distribution. The measure is popular in finance because of the subadditivity property, but it is very sensitive to a few outliers in the tail of the distribution. We propose a new classification method, extended robust SVM (ER-SVM), which minimizes an intermediate risk measure between the CVaR and value at risk (VaR) by expecting that the resulting model becomes less sensitive than ν-SVM to outliers. We can regard ER-SVM as an extension of robust SVM, which uses a truncated hinge loss. Numerical experiments imply the ER-SVM's possibility of achieving a better prediction performance with proper parameter setting.

Genetic algorithm based feature selection combined with dual classification for the automated detection of proliferative diabetic retinopathy.

PubMed

Welikala, R A; Fraz, M M; Dehmeshki, J; Hoppe, A; Tah, V; Mann, S; Williamson, T H; Barman, S A

2015-07-01

Proliferative diabetic retinopathy (PDR) is a condition that carries a high risk of severe visual impairment. The hallmark of PDR is the growth of abnormal new vessels. In this paper, an automated method for the detection of new vessels from retinal images is presented. This method is based on a dual classification approach. Two vessel segmentation approaches are applied to create two separate binary vessel map which each hold vital information. Local morphology features are measured from each binary vessel map to produce two separate 4-D feature vectors. Independent classification is performed for each feature vector using a support vector machine (SVM) classifier. The system then combines these individual outcomes to produce a final decision. This is followed by the creation of additional features to generate 21-D feature vectors, which feed into a genetic algorithm based feature selection approach with the objective of finding feature subsets that improve the performance of the classification. Sensitivity and specificity results using a dataset of 60 images are 0.9138 and 0.9600, respectively, on a per patch basis and 1.000 and 0.975, respectively, on a per image basis. Copyright © 2015 Elsevier Ltd. All rights reserved.

Chinese Sentence Classification Based on Convolutional Neural Network

NASA Astrophysics Data System (ADS)

Gu, Chengwei; Wu, Ming; Zhang, Chuang

2017-10-01

Sentence classification is one of the significant issues in Natural Language Processing (NLP). Feature extraction is often regarded as the key point for natural language processing. Traditional ways based on machine learning can not take high level features into consideration, such as Naive Bayesian Model. The neural network for sentence classification can make use of contextual information to achieve greater results in sentence classification tasks. In this paper, we focus on classifying Chinese sentences. And the most important is that we post a novel architecture of Convolutional Neural Network (CNN) to apply on Chinese sentence classification. In particular, most of the previous methods often use softmax classifier for prediction, we embed a linear support vector machine to substitute softmax in the deep neural network model, minimizing a margin-based loss to get a better result. And we use tanh as an activation function, instead of ReLU. The CNN model improve the result of Chinese sentence classification tasks. Experimental results on the Chinese news title database validate the effectiveness of our model.

A Machine-Learning Algorithm Toward Color Analysis for Chronic Liver Disease Classification, Employing Ultrasound Shear Wave Elastography.

PubMed

Gatos, Ilias; Tsantis, Stavros; Spiliopoulos, Stavros; Karnabatidis, Dimitris; Theotokas, Ioannis; Zoumpoulis, Pavlos; Loupas, Thanasis; Hazle, John D; Kagadis, George C

2017-09-01

The purpose of the present study was to employ a computer-aided diagnosis system that classifies chronic liver disease (CLD) using ultrasound shear wave elastography (SWE) imaging, with a stiffness value-clustering and machine-learning algorithm. A clinical data set of 126 patients (56 healthy controls, 70 with CLD) was analyzed. First, an RGB-to-stiffness inverse mapping technique was employed. A five-cluster segmentation was then performed associating corresponding different-color regions with certain stiffness value ranges acquired from the SWE manufacturer-provided color bar. Subsequently, 35 features (7 for each cluster), indicative of physical characteristics existing within the SWE image, were extracted. A stepwise regression analysis toward feature reduction was used to derive a reduced feature subset that was fed into the support vector machine classification algorithm to classify CLD from healthy cases. The highest accuracy in classification of healthy to CLD subject discrimination from the support vector machine model was 87.3% with sensitivity and specificity values of 93.5% and 81.2%, respectively. Receiver operating characteristic curve analysis gave an area under the curve value of 0.87 (confidence interval: 0.77-0.92). A machine-learning algorithm that quantifies color information in terms of stiffness values from SWE images and discriminates CLD from healthy cases is introduced. New objective parameters and criteria for CLD diagnosis employing SWE images provided by the present study can be considered an important step toward color-based interpretation, and could assist radiologists' diagnostic performance on a daily basis after being installed in a PC and employed retrospectively, immediately after the examination. Copyright © 2017 World Federation for Ultrasound in Medicine & Biology. Published by Elsevier Inc. All rights reserved.

Framework for Infectious Disease Analysis: A comprehensive and integrative multi-modeling approach to disease prediction and management.

PubMed

Erraguntla, Madhav; Zapletal, Josef; Lawley, Mark

2017-12-01

The impact of infectious disease on human populations is a function of many factors including environmental conditions, vector dynamics, transmission mechanics, social and cultural behaviors, and public policy. A comprehensive framework for disease management must fully connect the complete disease lifecycle, including emergence from reservoir populations, zoonotic vector transmission, and impact on human societies. The Framework for Infectious Disease Analysis is a software environment and conceptual architecture for data integration, situational awareness, visualization, prediction, and intervention assessment. Framework for Infectious Disease Analysis automatically collects biosurveillance data using natural language processing, integrates structured and unstructured data from multiple sources, applies advanced machine learning, and uses multi-modeling for analyzing disease dynamics and testing interventions in complex, heterogeneous populations. In the illustrative case studies, natural language processing from social media, news feeds, and websites was used for information extraction, biosurveillance, and situation awareness. Classification machine learning algorithms (support vector machines, random forests, and boosting) were used for disease predictions.

Classifying Structures in the ISM with Machine Learning Techniques

NASA Astrophysics Data System (ADS)

Beaumont, Christopher; Goodman, A. A.; Williams, J. P.

2011-01-01

The processes which govern molecular cloud evolution and star formation often sculpt structures in the ISM: filaments, pillars, shells, outflows, etc. Because of their morphological complexity, these objects are often identified manually. Manual classification has several disadvantages; the process is subjective, not easily reproducible, and does not scale well to handle increasingly large datasets. We have explored to what extent machine learning algorithms can be trained to autonomously identify specific morphological features in molecular cloud datasets. We show that the Support Vector Machine algorithm can successfully locate filaments and outflows blended with other emission structures. When the objects of interest are morphologically distinct from the surrounding emission, this autonomous classification achieves >90% accuracy. We have developed a set of IDL-based tools to apply this technique to other datasets.

Novel method of finding extreme edges in a convex set of N-dimension vectors

NASA Astrophysics Data System (ADS)

Hu, Chia-Lun J.

2001-11-01

As we published in the last few years, for a binary neural network pattern recognition system to learn a given mapping {Um mapped to Vm, m=1 to M} where um is an N- dimension analog (pattern) vector, Vm is a P-bit binary (classification) vector, the if-and-only-if (IFF) condition that this network can learn this mapping is that each i-set in {Ymi, m=1 to M} (where Ymithere existsVmiUm and Vmi=+1 or -1, is the i-th bit of VR-m).)(i=1 to P and there are P sets included here.) Is POSITIVELY, LINEARLY, INDEPENDENT or PLI. We have shown that this PLI condition is MORE GENERAL than the convexity condition applied to a set of N-vectors. In the design of old learning machines, we know that if a set of N-dimension analog vectors form a convex set, and if the machine can learn the boundary vectors (or extreme edges) of this set, then it can definitely learn the inside vectors contained in this POLYHEDRON CONE. This paper reports a new method and new algorithm to find the boundary vectors of a convex set of ND analog vectors.

Pre-operative prediction of surgical morbidity in children: comparison of five statistical models.

PubMed

Cooper, Jennifer N; Wei, Lai; Fernandez, Soledad A; Minneci, Peter C; Deans, Katherine J

2015-02-01

The accurate prediction of surgical risk is important to patients and physicians. Logistic regression (LR) models are typically used to estimate these risks. However, in the fields of data mining and machine-learning, many alternative classification and prediction algorithms have been developed. This study aimed to compare the performance of LR to several data mining algorithms for predicting 30-day surgical morbidity in children. We used the 2012 National Surgical Quality Improvement Program-Pediatric dataset to compare the performance of (1) a LR model that assumed linearity and additivity (simple LR model) (2) a LR model incorporating restricted cubic splines and interactions (flexible LR model) (3) a support vector machine, (4) a random forest and (5) boosted classification trees for predicting surgical morbidity. The ensemble-based methods showed significantly higher accuracy, sensitivity, specificity, PPV, and NPV than the simple LR model. However, none of the models performed better than the flexible LR model in terms of the aforementioned measures or in model calibration or discrimination. Support vector machines, random forests, and boosted classification trees do not show better performance than LR for predicting pediatric surgical morbidity. After further validation, the flexible LR model derived in this study could be used to assist with clinical decision-making based on patient-specific surgical risks. Copyright © 2014 Elsevier Ltd. All rights reserved.

sw-SVM: sensor weighting support vector machines for EEG-based brain-computer interfaces.

PubMed

Jrad, N; Congedo, M; Phlypo, R; Rousseau, S; Flamary, R; Yger, F; Rakotomamonjy, A

2011-10-01

In many machine learning applications, like brain-computer interfaces (BCI), high-dimensional sensor array data are available. Sensor measurements are often highly correlated and signal-to-noise ratio is not homogeneously spread across sensors. Thus, collected data are highly variable and discrimination tasks are challenging. In this work, we focus on sensor weighting as an efficient tool to improve the classification procedure. We present an approach integrating sensor weighting in the classification framework. Sensor weights are considered as hyper-parameters to be learned by a support vector machine (SVM). The resulting sensor weighting SVM (sw-SVM) is designed to satisfy a margin criterion, that is, the generalization error. Experimental studies on two data sets are presented, a P300 data set and an error-related potential (ErrP) data set. For the P300 data set (BCI competition III), for which a large number of trials is available, the sw-SVM proves to perform equivalently with respect to the ensemble SVM strategy that won the competition. For the ErrP data set, for which a small number of trials are available, the sw-SVM shows superior performances as compared to three state-of-the art approaches. Results suggest that the sw-SVM promises to be useful in event-related potentials classification, even with a small number of training trials.

Establishing glucose- and ABA-regulated transcription networks in Arabidopsis by microarray analysis and promoter classification using a Relevance Vector Machine.

PubMed

Li, Yunhai; Lee, Kee Khoon; Walsh, Sean; Smith, Caroline; Hadingham, Sophie; Sorefan, Karim; Cawley, Gavin; Bevan, Michael W

2006-03-01

Establishing transcriptional regulatory networks by analysis of gene expression data and promoter sequences shows great promise. We developed a novel promoter classification method using a Relevance Vector Machine (RVM) and Bayesian statistical principles to identify discriminatory features in the promoter sequences of genes that can correctly classify transcriptional responses. The method was applied to microarray data obtained from Arabidopsis seedlings treated with glucose or abscisic acid (ABA). Of those genes showing >2.5-fold changes in expression level, approximately 70% were correctly predicted as being up- or down-regulated (under 10-fold cross-validation), based on the presence or absence of a small set of discriminative promoter motifs. Many of these motifs have known regulatory functions in sugar- and ABA-mediated gene expression. One promoter motif that was not known to be involved in glucose-responsive gene expression was identified as the strongest classifier of glucose-up-regulated gene expression. We show it confers glucose-responsive gene expression in conjunction with another promoter motif, thus validating the classification method. We were able to establish a detailed model of glucose and ABA transcriptional regulatory networks and their interactions, which will help us to understand the mechanisms linking metabolism with growth in Arabidopsis. This study shows that machine learning strategies coupled to Bayesian statistical methods hold significant promise for identifying functionally significant promoter sequences.

Improved RMR Rock Mass Classification Using Artificial Intelligence Algorithms

NASA Astrophysics Data System (ADS)

Gholami, Raoof; Rasouli, Vamegh; Alimoradi, Andisheh

2013-09-01

Rock mass classification systems such as rock mass rating (RMR) are very reliable means to provide information about the quality of rocks surrounding a structure as well as to propose suitable support systems for unstable regions. Many correlations have been proposed to relate measured quantities such as wave velocity to rock mass classification systems to limit the associated time and cost of conducting the sampling and mechanical tests conventionally used to calculate RMR values. However, these empirical correlations have been found to be unreliable, as they usually overestimate or underestimate the RMR value. The aim of this paper is to compare the results of RMR classification obtained from the use of empirical correlations versus machine-learning methodologies based on artificial intelligence algorithms. The proposed methods were verified based on two case studies located in northern Iran. Relevance vector regression (RVR) and support vector regression (SVR), as two robust machine-learning methodologies, were used to predict the RMR for tunnel host rocks. RMR values already obtained by sampling and site investigation at one tunnel were taken into account as the output of the artificial networks during training and testing phases. The results reveal that use of empirical correlations overestimates the predicted RMR values. RVR and SVR, however, showed more reliable results, and are therefore suggested for use in RMR classification for design purposes of rock structures.

Comparison of support vector machine classification to partial least squares dimension reduction with logistic descrimination of hyperspectral data

NASA Astrophysics Data System (ADS)

Wilson, Machelle; Ustin, Susan L.; Rocke, David

2003-03-01

Remote sensing technologies with high spatial and spectral resolution show a great deal of promise in addressing critical environmental monitoring issues, but the ability to analyze and interpret the data lags behind the technology. Robust analytical methods are required before the wealth of data available through remote sensing can be applied to a wide range of environmental problems for which remote detection is the best method. In this study we compare the classification effectiveness of two relatively new techniques on data consisting of leaf-level reflectance from plants that have been exposed to varying levels of heavy metal toxicity. If these methodologies work well on leaf-level data, then there is some hope that they will also work well on data from airborne and space-borne platforms. The classification methods compared were support vector machine classification of exposed and non-exposed plants based on the reflectance data, and partial east squares compression of the reflectance data followed by classification using logistic discrimination (PLS/LD). PLS/LD was performed in two ways. We used the continuous concentration data as the response during compression, and then used the binary response required during logistic discrimination. We also used a binary response during compression followed by logistic discrimination. The statistics we used to compare the effectiveness of the methodologies was the leave-one-out cross validation estimate of the prediction error.

Advanced Methods for Passive Acoustic Detection, Classification, and Localization of Marine Mammals

DTIC Science & Technology

2014-09-30

floor 1176 Howell St Newport RI 02842 phone: (401) 832-5749 fax: (401) 832-4441 email: David.Moretti@navy.mil Steve W. Martin SPAWAR...APPROACH Odontocete click detection and classification. A multi-class support vector machine (SVM) classifier was previously developed ( Jarvis ...beaked whales, Risso’s dolphins, short-finned pilot whales, and sperm whales. Here Moretti’s group, particularly S. Jarvis , is improving the SVM

Advanced Methods for Passive Acoustic Detection, Classification, and Localization of Marine Mammals

DTIC Science & Technology

2011-09-30

Newport RI 02842 phone: (401) 832-5749 fax: (401) 832-4441 email: David.Moretti@navy.mil Steve W. Martin SPAWAR Systems Center Pacific...APPROACH Odontocete click detection and classification. A multiclass support vector machine (SVM) classifier was previously developed ( Jarvis et...beaked whales, Risso’s dolphins, short-finned pilot whales, and sperm whales. Here Moretti’s group, especially S. Jarvis , will improve the SVM classifier

A Novel Approach for Lie Detection Based on F-Score and Extreme Learning Machine

PubMed Central

Gao, Junfeng; Wang, Zhao; Yang, Yong; Zhang, Wenjia; Tao, Chunyi; Guan, Jinan; Rao, Nini

2013-01-01

A new machine learning method referred to as F-score_ELM was proposed to classify the lying and truth-telling using the electroencephalogram (EEG) signals from 28 guilty and innocent subjects. Thirty-one features were extracted from the probe responses from these subjects. Then, a recently-developed classifier called extreme learning machine (ELM) was combined with F-score, a simple but effective feature selection method, to jointly optimize the number of the hidden nodes of ELM and the feature subset by a grid-searching training procedure. The method was compared to two classification models combining principal component analysis with back-propagation network and support vector machine classifiers. We thoroughly assessed the performance of these classification models including the training and testing time, sensitivity and specificity from the training and testing sets, as well as network size. The experimental results showed that the number of the hidden nodes can be effectively optimized by the proposed method. Also, F-score_ELM obtained the best classification accuracy and required the shortest training and testing time. PMID:23755136

Epileptic seizure detection from EEG signals with phase-amplitude cross-frequency coupling and support vector machine

NASA Astrophysics Data System (ADS)

Liu, Yang; Wang, Jiang; Cai, Lihui; Chen, Yingyuan; Qin, Yingmei

2018-03-01

As a pattern of cross-frequency coupling (CFC), phase-amplitude coupling (PAC) depicts the interaction between the phase and amplitude of distinct frequency bands from the same signal, and has been proved to be closely related to the brain’s cognitive and memory activities. This work utilized PAC and support vector machine (SVM) classifier to identify the epileptic seizures from electroencephalogram (EEG) data. The entropy-based modulation index (MI) matrixes are used to express the strength of PAC, from which we extracted features as the input for classifier. Based on the Bonn database, which contains five datasets of EEG segments obtained from healthy volunteers and epileptic subjects, a 100% classification accuracy is achieved for identifying seizure ictal from healthy data, and an accuracy of 97.67% is reached in the classification of ictal EEG signals from inter-ictal EEGs. Based on the CHB-MIT database which is a group of continuously recorded epileptic EEGs by scalp electrodes, a 97.50% classification accuracy is obtained and a raising sign of MI value is found at 6s before seizure onset. The classification performance in this work is effective, and PAC can be considered as a useful tool for detecting and predicting the epileptic seizures and providing reference for clinical diagnosis.

A machine learning approach to galaxy-LSS classification - I. Imprints on halo merger trees

NASA Astrophysics Data System (ADS)

Hui, Jianan; Aragon, Miguel; Cui, Xinping; Flegal, James M.

2018-04-01

The cosmic web plays a major role in the formation and evolution of galaxies and defines, to a large extent, their properties. However, the relation between galaxies and environment is still not well understood. Here, we present a machine learning approach to study imprints of environmental effects on the mass assembly of haloes. We present a galaxy-LSS machine learning classifier based on galaxy properties sensitive to the environment. We then use the classifier to assess the relevance of each property. Correlations between galaxy properties and their cosmic environment can be used to predict galaxy membership to void/wall or filament/cluster with an accuracy of 93 per cent. Our study unveils environmental information encoded in properties of haloes not normally considered directly dependent on the cosmic environment such as merger history and complexity. Understanding the physical mechanism by which the cosmic web is imprinted in a halo can lead to significant improvements in galaxy formation models. This is accomplished by extracting features from galaxy properties and merger trees, computing feature scores for each feature and then applying support vector machine (SVM) to different feature sets. To this end, we have discovered that the shape and depth of the merger tree, formation time, and density of the galaxy are strongly associated with the cosmic environment. We describe a significant improvement in the original classification algorithm by performing LU decomposition of the distance matrix computed by the feature vectors and then using the output of the decomposition as input vectors for SVM.

Real-data comparison of data mining methods in prediction of diabetes in iran.

PubMed

Tapak, Lily; Mahjub, Hossein; Hamidi, Omid; Poorolajal, Jalal

2013-09-01

Diabetes is one of the most common non-communicable diseases in developing countries. Early screening and diagnosis play an important role in effective prevention strategies. This study compared two traditional classification methods (logistic regression and Fisher linear discriminant analysis) and four machine-learning classifiers (neural networks, support vector machines, fuzzy c-mean, and random forests) to classify persons with and without diabetes. The data set used in this study included 6,500 subjects from the Iranian national non-communicable diseases risk factors surveillance obtained through a cross-sectional survey. The obtained sample was based on cluster sampling of the Iran population which was conducted in 2005-2009 to assess the prevalence of major non-communicable disease risk factors. Ten risk factors that are commonly associated with diabetes were selected to compare the performance of six classifiers in terms of sensitivity, specificity, total accuracy, and area under the receiver operating characteristic (ROC) curve criteria. Support vector machines showed the highest total accuracy (0.986) as well as area under the ROC (0.979). Also, this method showed high specificity (1.000) and sensitivity (0.820). All other methods produced total accuracy of more than 85%, but for all methods, the sensitivity values were very low (less than 0.350). The results of this study indicate that, in terms of sensitivity, specificity, and overall classification accuracy, the support vector machine model ranks first among all the classifiers tested in the prediction of diabetes. Therefore, this approach is a promising classifier for predicting diabetes, and it should be further investigated for the prediction of other diseases.

Condition Assessment of Foundation Piles and Utility Poles Based on Guided Wave Propagation Using a Network of Tactile Transducers and Support Vector Machines

PubMed Central

Yu, Yang; Niederleithinger, Ernst; Li, Jianchun; Wiggenhauser, Herbert

2017-01-01

This paper presents a novel non-destructive testing and health monitoring system using a network of tactile transducers and accelerometers for the condition assessment and damage classification of foundation piles and utility poles. While in traditional pile integrity testing an impact hammer with broadband frequency excitation is typically used, the proposed testing system utilizes an innovative excitation system based on a network of tactile transducers to induce controlled narrow-band frequency stress waves. Thereby, the simultaneous excitation of multiple stress wave types and modes is avoided (or at least reduced), and targeted wave forms can be generated. The new testing system enables the testing and monitoring of foundation piles and utility poles where the top is inaccessible, making the new testing system suitable, for example, for the condition assessment of pile structures with obstructed heads and of poles with live wires. For system validation, the new system was experimentally tested on nine timber and concrete poles that were inflicted with several types of damage. The tactile transducers were excited with continuous sine wave signals of 1 kHz frequency. Support vector machines were employed together with advanced signal processing algorithms to distinguish recorded stress wave signals from pole structures with different types of damage. The results show that using fast Fourier transform signals, combined with principal component analysis as the input feature vector for support vector machine (SVM) classifiers with different kernel functions, can achieve damage classification with accuracies of 92.5% ± 7.5%. PMID:29258274

Learning machines and sleeping brains: Automatic sleep stage classification using decision-tree multi-class support vector machines.

PubMed

Lajnef, Tarek; Chaibi, Sahbi; Ruby, Perrine; Aguera, Pierre-Emmanuel; Eichenlaub, Jean-Baptiste; Samet, Mounir; Kachouri, Abdennaceur; Jerbi, Karim

2015-07-30

Sleep staging is a critical step in a range of electrophysiological signal processing pipelines used in clinical routine as well as in sleep research. Although the results currently achievable with automatic sleep staging methods are promising, there is need for improvement, especially given the time-consuming and tedious nature of visual sleep scoring. Here we propose a sleep staging framework that consists of a multi-class support vector machine (SVM) classification based on a decision tree approach. The performance of the method was evaluated using polysomnographic data from 15 subjects (electroencephalogram (EEG), electrooculogram (EOG) and electromyogram (EMG) recordings). The decision tree, or dendrogram, was obtained using a hierarchical clustering technique and a wide range of time and frequency-domain features were extracted. Feature selection was carried out using forward sequential selection and classification was evaluated using k-fold cross-validation. The dendrogram-based SVM (DSVM) achieved mean specificity, sensitivity and overall accuracy of 0.92, 0.74 and 0.88 respectively, compared to expert visual scoring. Restricting DSVM classification to data where both experts' scoring was consistent (76.73% of the data) led to a mean specificity, sensitivity and overall accuracy of 0.94, 0.82 and 0.92 respectively. The DSVM framework outperforms classification with more standard multi-class "one-against-all" SVM and linear-discriminant analysis. The promising results of the proposed methodology suggest that it may be a valuable alternative to existing automatic methods and that it could accelerate visual scoring by providing a robust starting hypnogram that can be further fine-tuned by expert inspection. Copyright © 2015 Elsevier B.V. All rights reserved.

A support vector machine for spectral classification of emission-line galaxies from the Sloan Digital Sky Survey

NASA Astrophysics Data System (ADS)

Shi, Fei; Liu, Yu-Yan; Sun, Guang-Lan; Li, Pei-Yu; Lei, Yu-Ming; Wang, Jian

2015-10-01

The emission-lines of galaxies originate from massive young stars or supermassive blackholes. As a result, spectral classification of emission-line galaxies into star-forming galaxies, active galactic nucleus (AGN) hosts, or compositions of both relates closely to formation and evolution of galaxy. To find efficient and automatic spectral classification method, especially in large surveys and huge data bases, a support vector machine (SVM) supervised learning algorithm is applied to a sample of emission-line galaxies from the Sloan Digital Sky Survey (SDSS) data release 9 (DR9) provided by the Max Planck Institute and the Johns Hopkins University (MPA/JHU). A two-step approach is adopted. (i) The SVM must be trained with a subset of objects that are known to be AGN hosts, composites or star-forming galaxies, treating the strong emission-line flux measurements as input feature vectors in an n-dimensional space, where n is the number of strong emission-line flux ratios. (ii) After training on a sample of emission-line galaxies, the remaining galaxies are automatically classified. In the classification process, we use a 10-fold cross-validation technique. We show that the classification diagrams based on the [N II]/Hα versus other emission-line ratio, such as [O III]/Hβ, [Ne III]/[O II], ([O III]λ4959+[O III]λ5007)/[O III]λ4363, [O II]/Hβ, [Ar III]/[O III], [S II]/Hα, and [O I]/Hα, plus colour, allows us to separate unambiguously AGN hosts, composites or star-forming galaxies. Among them, the diagram of [N II]/Hα versus [O III]/Hβ achieved an accuracy of 99 per cent to separate the three classes of objects. The other diagrams above give an accuracy of ˜91 per cent.

Machine learning algorithms for meteorological event classification in the coastal area using in-situ data

NASA Astrophysics Data System (ADS)

Sokolov, Anton; Gengembre, Cyril; Dmitriev, Egor; Delbarre, Hervé

2017-04-01

The problem is considered of classification of local atmospheric meteorological events in the coastal area such as sea breezes, fogs and storms. The in-situ meteorological data as wind speed and direction, temperature, humidity and turbulence are used as predictors. Local atmospheric events of 2013-2014 were analysed manually to train classification algorithms in the coastal area of English Channel in Dunkirk (France). Then, ultrasonic anemometer data and LIDAR wind profiler data were used as predictors. A few algorithms were applied to determine meteorological events by local data such as a decision tree, the nearest neighbour classifier, a support vector machine. The comparison of classification algorithms was carried out, the most important predictors for each event type were determined. It was shown that in more than 80 percent of the cases machine learning algorithms detect the meteorological class correctly. We expect that this methodology could be applied also to classify events by climatological in-situ data or by modelling data. It allows estimating frequencies of each event in perspective of climate change.

Machine learning in soil classification.

PubMed

Bhattacharya, B; Solomatine, D P

2006-03-01

In a number of engineering problems, e.g. in geotechnics, petroleum engineering, etc. intervals of measured series data (signals) are to be attributed a class maintaining the constraint of contiguity and standard classification methods could be inadequate. Classification in this case needs involvement of an expert who observes the magnitude and trends of the signals in addition to any a priori information that might be available. In this paper, an approach for automating this classification procedure is presented. Firstly, a segmentation algorithm is developed and applied to segment the measured signals. Secondly, the salient features of these segments are extracted using boundary energy method. Based on the measured data and extracted features to assign classes to the segments classifiers are built; they employ Decision Trees, ANN and Support Vector Machines. The methodology was tested in classifying sub-surface soil using measured data from Cone Penetration Testing and satisfactory results were obtained.

Classification of older adults with/without a fall history using machine learning methods.

PubMed

Lin Zhang; Ou Ma; Fabre, Jennifer M; Wood, Robert H; Garcia, Stephanie U; Ivey, Kayla M; McCann, Evan D

2015-01-01

Falling is a serious problem in an aged society such that assessment of the risk of falls for individuals is imperative for the research and practice of falls prevention. This paper introduces an application of several machine learning methods for training a classifier which is capable of classifying individual older adults into a high risk group and a low risk group (distinguished by whether or not the members of the group have a recent history of falls). Using a 3D motion capture system, significant gait features related to falls risk are extracted. By training these features, classification hypotheses are obtained based on machine learning techniques (K Nearest-neighbour, Naive Bayes, Logistic Regression, Neural Network, and Support Vector Machine). Training and test accuracies with sensitivity and specificity of each of these techniques are assessed. The feature adjustment and tuning of the machine learning algorithms are discussed. The outcome of the study will benefit the prediction and prevention of falls.

Pattern classification using an olfactory model with PCA feature selection in electronic noses: study and application.

PubMed

Fu, Jun; Huang, Canqin; Xing, Jianguo; Zheng, Junbao

2012-01-01

Biologically-inspired models and algorithms are considered as promising sensor array signal processing methods for electronic noses. Feature selection is one of the most important issues for developing robust pattern recognition models in machine learning. This paper describes an investigation into the classification performance of a bionic olfactory model with the increase of the dimensions of input feature vector (outer factor) as well as its parallel channels (inner factor). The principal component analysis technique was applied for feature selection and dimension reduction. Two data sets of three classes of wine derived from different cultivars and five classes of green tea derived from five different provinces of China were used for experiments. In the former case the results showed that the average correct classification rate increased as more principal components were put in to feature vector. In the latter case the results showed that sufficient parallel channels should be reserved in the model to avoid pattern space crowding. We concluded that 6~8 channels of the model with principal component feature vector values of at least 90% cumulative variance is adequate for a classification task of 3~5 pattern classes considering the trade-off between time consumption and classification rate.

Machine learning methods for the classification of gliomas: Initial results using features extracted from MR spectroscopy.

PubMed

Ranjith, G; Parvathy, R; Vikas, V; Chandrasekharan, Kesavadas; Nair, Suresh

2015-04-01

With the advent of new imaging modalities, radiologists are faced with handling increasing volumes of data for diagnosis and treatment planning. The use of automated and intelligent systems is becoming essential in such a scenario. Machine learning, a branch of artificial intelligence, is increasingly being used in medical image analysis applications such as image segmentation, registration and computer-aided diagnosis and detection. Histopathological analysis is currently the gold standard for classification of brain tumors. The use of machine learning algorithms along with extraction of relevant features from magnetic resonance imaging (MRI) holds promise of replacing conventional invasive methods of tumor classification. The aim of the study is to classify gliomas into benign and malignant types using MRI data. Retrospective data from 28 patients who were diagnosed with glioma were used for the analysis. WHO Grade II (low-grade astrocytoma) was classified as benign while Grade III (anaplastic astrocytoma) and Grade IV (glioblastoma multiforme) were classified as malignant. Features were extracted from MR spectroscopy. The classification was done using four machine learning algorithms: multilayer perceptrons, support vector machine, random forest and locally weighted learning. Three of the four machine learning algorithms gave an area under ROC curve in excess of 0.80. Random forest gave the best performance in terms of AUC (0.911) while sensitivity was best for locally weighted learning (86.1%). The performance of different machine learning algorithms in the classification of gliomas is promising. An even better performance may be expected by integrating features extracted from other MR sequences. © The Author(s) 2015 Reprints and permissions: sagepub.co.uk/journalsPermissions.nav.

Semisupervised Support Vector Machines With Tangent Space Intrinsic Manifold Regularization.

PubMed

Sun, Shiliang; Xie, Xijiong

2016-09-01

Semisupervised learning has been an active research topic in machine learning and data mining. One main reason is that labeling examples is expensive and time-consuming, while there are large numbers of unlabeled examples available in many practical problems. So far, Laplacian regularization has been widely used in semisupervised learning. In this paper, we propose a new regularization method called tangent space intrinsic manifold regularization. It is intrinsic to data manifold and favors linear functions on the manifold. Fundamental elements involved in the formulation of the regularization are local tangent space representations, which are estimated by local principal component analysis, and the connections that relate adjacent tangent spaces. Simultaneously, we explore its application to semisupervised classification and propose two new learning algorithms called tangent space intrinsic manifold regularized support vector machines (TiSVMs) and tangent space intrinsic manifold regularized twin SVMs (TiTSVMs). They effectively integrate the tangent space intrinsic manifold regularization consideration. The optimization of TiSVMs can be solved by a standard quadratic programming, while the optimization of TiTSVMs can be solved by a pair of standard quadratic programmings. The experimental results of semisupervised classification problems show the effectiveness of the proposed semisupervised learning algorithms.

Propensity score estimation: neural networks, support vector machines, decision trees (CART), and meta-classifiers as alternatives to logistic regression.

PubMed

Westreich, Daniel; Lessler, Justin; Funk, Michele Jonsson

2010-08-01

Propensity scores for the analysis of observational data are typically estimated using logistic regression. Our objective in this review was to assess machine learning alternatives to logistic regression, which may accomplish the same goals but with fewer assumptions or greater accuracy. We identified alternative methods for propensity score estimation and/or classification from the public health, biostatistics, discrete mathematics, and computer science literature, and evaluated these algorithms for applicability to the problem of propensity score estimation, potential advantages over logistic regression, and ease of use. We identified four techniques as alternatives to logistic regression: neural networks, support vector machines, decision trees (classification and regression trees [CART]), and meta-classifiers (in particular, boosting). Although the assumptions of logistic regression are well understood, those assumptions are frequently ignored. All four alternatives have advantages and disadvantages compared with logistic regression. Boosting (meta-classifiers) and, to a lesser extent, decision trees (particularly CART), appear to be most promising for use in the context of propensity score analysis, but extensive simulation studies are needed to establish their utility in practice. Copyright (c) 2010 Elsevier Inc. All rights reserved.

A novel approach for detection and classification of mammographic microcalcifications using wavelet analysis and extreme learning machine.

PubMed

Malar, E; Kandaswamy, A; Chakravarthy, D; Giri Dharan, A

2012-09-01

The objective of this paper is to reveal the effectiveness of wavelet based tissue texture analysis for microcalcification detection in digitized mammograms using Extreme Learning Machine (ELM). Microcalcifications are tiny deposits of calcium in the breast tissue which are potential indicators for early detection of breast cancer. The dense nature of the breast tissue and the poor contrast of the mammogram image prohibit the effectiveness in identifying microcalcifications. Hence, a new approach to discriminate the microcalcifications from the normal tissue is done using wavelet features and is compared with different feature vectors extracted using Gray Level Spatial Dependence Matrix (GLSDM) and Gabor filter based techniques. A total of 120 Region of Interests (ROIs) extracted from 55 mammogram images of mini-Mias database, including normal and microcalcification images are used in the current research. The network is trained with the above mentioned features and the results denote that ELM produces relatively better classification accuracy (94%) with a significant reduction in training time than the other artificial neural networks like Bayesnet classifier, Naivebayes classifier, and Support Vector Machine. ELM also avoids problems like local minima, improper learning rate, and over fitting. Copyright © 2012 Elsevier Ltd. All rights reserved.

Multi-view L2-SVM and its multi-view core vector machine.

PubMed

Huang, Chengquan; Chung, Fu-lai; Wang, Shitong

2016-03-01

In this paper, a novel L2-SVM based classifier Multi-view L2-SVM is proposed to address multi-view classification tasks. The proposed Multi-view L2-SVM classifier does not have any bias in its objective function and hence has the flexibility like μ-SVC in the sense that the number of the yielded support vectors can be controlled by a pre-specified parameter. The proposed Multi-view L2-SVM classifier can make full use of the coherence and the difference of different views through imposing the consensus among multiple views to improve the overall classification performance. Besides, based on the generalized core vector machine GCVM, the proposed Multi-view L2-SVM classifier is extended into its GCVM version MvCVM which can realize its fast training on large scale multi-view datasets, with its asymptotic linear time complexity with the sample size and its space complexity independent of the sample size. Our experimental results demonstrated the effectiveness of the proposed Multi-view L2-SVM classifier for small scale multi-view datasets and the proposed MvCVM classifier for large scale multi-view datasets. Copyright © 2015 Elsevier Ltd. All rights reserved.

Support Vector Machine-based classification of protein folds using the structural properties of amino acid residues and amino acid residue pairs.

PubMed

Shamim, Mohammad Tabrez Anwar; Anwaruddin, Mohammad; Nagarajaram, H A

2007-12-15

Fold recognition is a key step in the protein structure discovery process, especially when traditional sequence comparison methods fail to yield convincing structural homologies. Although many methods have been developed for protein fold recognition, their accuracies remain low. This can be attributed to insufficient exploitation of fold discriminatory features. We have developed a new method for protein fold recognition using structural information of amino acid residues and amino acid residue pairs. Since protein fold recognition can be treated as a protein fold classification problem, we have developed a Support Vector Machine (SVM) based classifier approach that uses secondary structural state and solvent accessibility state frequencies of amino acids and amino acid pairs as feature vectors. Among the individual properties examined secondary structural state frequencies of amino acids gave an overall accuracy of 65.2% for fold discrimination, which is better than the accuracy by any method reported so far in the literature. Combination of secondary structural state frequencies with solvent accessibility state frequencies of amino acids and amino acid pairs further improved the fold discrimination accuracy to more than 70%, which is approximately 8% higher than the best available method. In this study we have also tested, for the first time, an all-together multi-class method known as Crammer and Singer method for protein fold classification. Our studies reveal that the three multi-class classification methods, namely one versus all, one versus one and Crammer and Singer method, yield similar predictions. Dataset and stand-alone program are available upon request.

PHOTOMETRIC SUPERNOVA CLASSIFICATION WITH MACHINE LEARNING

DOE Office of Scientific and Technical Information (OSTI.GOV)

Lochner, Michelle; Peiris, Hiranya V.; Lahav, Ofer

Automated photometric supernova classification has become an active area of research in recent years in light of current and upcoming imaging surveys such as the Dark Energy Survey (DES) and the Large Synoptic Survey Telescope, given that spectroscopic confirmation of type for all supernovae discovered will be impossible. Here, we develop a multi-faceted classification pipeline, combining existing and new approaches. Our pipeline consists of two stages: extracting descriptive features from the light curves and classification using a machine learning algorithm. Our feature extraction methods vary from model-dependent techniques, namely SALT2 fits, to more independent techniques that fit parametric models tomore » curves, to a completely model-independent wavelet approach. We cover a range of representative machine learning algorithms, including naive Bayes, k -nearest neighbors, support vector machines, artificial neural networks, and boosted decision trees (BDTs). We test the pipeline on simulated multi-band DES light curves from the Supernova Photometric Classification Challenge. Using the commonly used area under the curve (AUC) of the Receiver Operating Characteristic as a metric, we find that the SALT2 fits and the wavelet approach, with the BDTs algorithm, each achieve an AUC of 0.98, where 1 represents perfect classification. We find that a representative training set is essential for good classification, whatever the feature set or algorithm, with implications for spectroscopic follow-up. Importantly, we find that by using either the SALT2 or the wavelet feature sets with a BDT algorithm, accurate classification is possible purely from light curve data, without the need for any redshift information.« less

Deep Restricted Kernel Machines Using Conjugate Feature Duality.

PubMed

Suykens, Johan A K

2017-08-01

The aim of this letter is to propose a theory of deep restricted kernel machines offering new foundations for deep learning with kernel machines. From the viewpoint of deep learning, it is partially related to restricted Boltzmann machines, which are characterized by visible and hidden units in a bipartite graph without hidden-to-hidden connections and deep learning extensions as deep belief networks and deep Boltzmann machines. From the viewpoint of kernel machines, it includes least squares support vector machines for classification and regression, kernel principal component analysis (PCA), matrix singular value decomposition, and Parzen-type models. A key element is to first characterize these kernel machines in terms of so-called conjugate feature duality, yielding a representation with visible and hidden units. It is shown how this is related to the energy form in restricted Boltzmann machines, with continuous variables in a nonprobabilistic setting. In this new framework of so-called restricted kernel machine (RKM) representations, the dual variables correspond to hidden features. Deep RKM are obtained by coupling the RKMs. The method is illustrated for deep RKM, consisting of three levels with a least squares support vector machine regression level and two kernel PCA levels. In its primal form also deep feedforward neural networks can be trained within this framework.

Design of Clinical Support Systems Using Integrated Genetic Algorithm and Support Vector Machine

NASA Astrophysics Data System (ADS)

Chen, Yung-Fu; Huang, Yung-Fa; Jiang, Xiaoyi; Hsu, Yuan-Nian; Lin, Hsuan-Hung

Clinical decision support system (CDSS) provides knowledge and specific information for clinicians to enhance diagnostic efficiency and improving healthcare quality. An appropriate CDSS can highly elevate patient safety, improve healthcare quality, and increase cost-effectiveness. Support vector machine (SVM) is believed to be superior to traditional statistical and neural network classifiers. However, it is critical to determine suitable combination of SVM parameters regarding classification performance. Genetic algorithm (GA) can find optimal solution within an acceptable time, and is faster than greedy algorithm with exhaustive searching strategy. By taking the advantage of GA in quickly selecting the salient features and adjusting SVM parameters, a method using integrated GA and SVM (IGS), which is different from the traditional method with GA used for feature selection and SVM for classification, was used to design CDSSs for prediction of successful ventilation weaning, diagnosis of patients with severe obstructive sleep apnea, and discrimination of different cell types form Pap smear. The results show that IGS is better than methods using SVM alone or linear discriminator.

A new technique for rapid assessment of eutrophication status of coastal waters using a support vector machine

NASA Astrophysics Data System (ADS)

Kong, Xianyu; Che, Xiaowei; Su, Rongguo; Zhang, Chuansong; Yao, Qingzhen; Shi, Xiaoyong

2017-05-01

There is an urgent need to develop efficient evaluation tools that use easily measured variables to make rapid and timely eutrophication assessments, which are important for marine health management, and to implement eutrophication monitoring programs. In this study, an approach for rapidly assessing the eutrophication status of coastal waters with three easily measured parameters (turbidity, chlorophyll a and dissolved oxygen) was developed by the grid search (GS) optimized support vector machine (SVM), with trophic index TRIX classification results as the reference. With the optimized penalty parameter C =64 and the kernel parameter γ =1, the classification accuracy rates reached 89.3% for the training data, 88.3% for the cross-validation, and 88.5% for the validation dataset. Because the developed approach only used three easy-to-measure variables, its application could facilitate the rapid assessment of the eutrophication status of coastal waters, resulting in potential cost savings in marine monitoring programs and assisting in the provision of timely advice for marine management.

Classification of skin cancer images using local binary pattern and SVM classifier

NASA Astrophysics Data System (ADS)

Adjed, Faouzi; Faye, Ibrahima; Ababsa, Fakhreddine; Gardezi, Syed Jamal; Dass, Sarat Chandra

2016-11-01

In this paper, a classification method for melanoma and non-melanoma skin cancer images has been presented using the local binary patterns (LBP). The LBP computes the local texture information from the skin cancer images, which is later used to compute some statistical features that have capability to discriminate the melanoma and non-melanoma skin tissues. Support vector machine (SVM) is applied on the feature matrix for classification into two skin image classes (malignant and benign). The method achieves good classification accuracy of 76.1% with sensitivity of 75.6% and specificity of 76.7%.

Automatic optical detection and classification of marine animals around MHK converters using machine vision

DOE Office of Scientific and Technical Information (OSTI.GOV)

Brunton, Steven

Optical systems provide valuable information for evaluating interactions and associations between organisms and MHK energy converters and for capturing potentially rare encounters between marine organisms and MHK device. The deluge of optical data from cabled monitoring packages makes expert review time-consuming and expensive. We propose algorithms and a processing framework to automatically extract events of interest from underwater video. The open-source software framework consists of background subtraction, filtering, feature extraction and hierarchical classification algorithms. This principle classification pipeline was validated on real-world data collected with an experimental underwater monitoring package. An event detection rate of 100% was achieved using robustmore » principal components analysis (RPCA), Fourier feature extraction and a support vector machine (SVM) binary classifier. The detected events were then further classified into more complex classes – algae | invertebrate | vertebrate, one species | multiple species of fish, and interest rank. Greater than 80% accuracy was achieved using a combination of machine learning techniques.« less

Wavelet images and Chou's pseudo amino acid composition for protein classification.

PubMed

Nanni, Loris; Brahnam, Sheryl; Lumini, Alessandra

2012-08-01

The last decade has seen an explosion in the collection of protein data. To actualize the potential offered by this wealth of data, it is important to develop machine systems capable of classifying and extracting features from proteins. Reliable machine systems for protein classification offer many benefits, including the promise of finding novel drugs and vaccines. In developing our system, we analyze and compare several feature extraction methods used in protein classification that are based on the calculation of texture descriptors starting from a wavelet representation of the protein. We then feed these texture-based representations of the protein into an Adaboost ensemble of neural network or a support vector machine classifier. In addition, we perform experiments that combine our feature extraction methods with a standard method that is based on the Chou's pseudo amino acid composition. Using several datasets, we show that our best approach outperforms standard methods. The Matlab code of the proposed protein descriptors is available at http://bias.csr.unibo.it/nanni/wave.rar .

HYBRID NEURAL NETWORK AND SUPPORT VECTOR MACHINE METHOD FOR OPTIMIZATION

NASA Technical Reports Server (NTRS)

Rai, Man Mohan (Inventor)

2005-01-01

System and method for optimization of a design associated with a response function, using a hybrid neural net and support vector machine (NN/SVM) analysis to minimize or maximize an objective function, optionally subject to one or more constraints. As a first example, the NN/SVM analysis is applied iteratively to design of an aerodynamic component, such as an airfoil shape, where the objective function measures deviation from a target pressure distribution on the perimeter of the aerodynamic component. As a second example, the NN/SVM analysis is applied to data classification of a sequence of data points in a multidimensional space. The NN/SVM analysis is also applied to data regression.

Hybrid Neural Network and Support Vector Machine Method for Optimization

NASA Technical Reports Server (NTRS)

Rai, Man Mohan (Inventor)

2007-01-01

System and method for optimization of a design associated with a response function, using a hybrid neural net and support vector machine (NN/SVM) analysis to minimize or maximize an objective function, optionally subject to one or more constraints. As a first example, the NN/SVM analysis is applied iteratively to design of an aerodynamic component, such as an airfoil shape, where the objective function measures deviation from a target pressure distribution on the perimeter of the aerodynamic component. As a second example, the NN/SVM analysis is applied to data classification of a sequence of data points in a multidimensional space. The NN/SVM analysis is also applied to data regression.

Identification of cigarette smoke inhalations from wearable sensor data using a Support Vector Machine classifier.

PubMed

Lopez-Meyer, Paulo; Tiffany, Stephen; Sazonov, Edward

2012-01-01

This study presents a subject-independent model for detection of smoke inhalations from wearable sensors capturing characteristic hand-to-mouth gestures and changes in breathing patterns during cigarette smoking. Wearable sensors were used to detect the proximity of the hand to the mouth and to acquire the respiratory patterns. The waveforms of sensor signals were used as features to build a Support Vector Machine classification model. Across a data set of 20 enrolled participants, precision of correct identification of smoke inhalations was found to be >87%, and a resulting recall >80%. These results suggest that it is possible to analyze smoking behavior by means of a wearable and non-invasive sensor system.

Detection of Dendritic Spines Using Wavelet Packet Entropy and Fuzzy Support Vector Machine.

PubMed

Wang, Shuihua; Li, Yang; Shao, Ying; Cattani, Carlo; Zhang, Yudong; Du, Sidan

2017-01-01

The morphology of dendritic spines is highly correlated with the neuron function. Therefore, it is of positive influence for the research of the dendritic spines. However, it is tried to manually label the spine types for statistical analysis. In this work, we proposed an approach based on the combination of wavelet contour analysis for the backbone detection, wavelet packet entropy, and fuzzy support vector machine for the spine classification. The experiments show that this approach is promising. The average detection accuracy of "MushRoom" achieves 97.3%, "Stubby" achieves 94.6%, and "Thin" achieves 97.2%. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

Balanced VS Imbalanced Training Data: Classifying Rapideye Data with Support Vector Machines

NASA Astrophysics Data System (ADS)

Ustuner, M.; Sanli, F. B.; Abdikan, S.

2016-06-01

The accuracy of supervised image classification is highly dependent upon several factors such as the design of training set (sample selection, composition, purity and size), resolution of input imagery and landscape heterogeneity. The design of training set is still a challenging issue since the sensitivity of classifier algorithm at learning stage is different for the same dataset. In this paper, the classification of RapidEye imagery with balanced and imbalanced training data for mapping the crop types was addressed. Classification with imbalanced training data may result in low accuracy in some scenarios. Support Vector Machines (SVM), Maximum Likelihood (ML) and Artificial Neural Network (ANN) classifications were implemented here to classify the data. For evaluating the influence of the balanced and imbalanced training data on image classification algorithms, three different training datasets were created. Two different balanced datasets which have 70 and 100 pixels for each class of interest and one imbalanced dataset in which each class has different number of pixels were used in classification stage. Results demonstrate that ML and NN classifications are affected by imbalanced training data in resulting a reduction in accuracy (from 90.94% to 85.94% for ML and from 91.56% to 88.44% for NN) while SVM is not affected significantly (from 94.38% to 94.69%) and slightly improved. Our results highlighted that SVM is proven to be a very robust, consistent and effective classifier as it can perform very well under balanced and imbalanced training data situations. Furthermore, the training stage should be precisely and carefully designed for the need of adopted classifier.

Testing the Potential of Vegetation Indices for Land Use/cover Classification Using High Resolution Data

NASA Astrophysics Data System (ADS)

Karakacan Kuzucu, A.; Bektas Balcik, F.

2017-11-01

Accurate and reliable land use/land cover (LULC) information obtained by remote sensing technology is necessary in many applications such as environmental monitoring, agricultural management, urban planning, hydrological applications, soil management, vegetation condition study and suitability analysis. But this information still remains a challenge especially in heterogeneous landscapes covering urban and rural areas due to spectrally similar LULC features. In parallel with technological developments, supplementary data such as satellite-derived spectral indices have begun to be used as additional bands in classification to produce data with high accuracy. The aim of this research is to test the potential of spectral vegetation indices combination with supervised classification methods and to extract reliable LULC information from SPOT 7 multispectral imagery. The Normalized Difference Vegetation Index (NDVI), the Ratio Vegetation Index (RATIO), the Soil Adjusted Vegetation Index (SAVI) were the three vegetation indices used in this study. The classical maximum likelihood classifier (MLC) and support vector machine (SVM) algorithm were applied to classify SPOT 7 image. Catalca is selected region located in the north west of the Istanbul in Turkey, which has complex landscape covering artificial surface, forest and natural area, agricultural field, quarry/mining area, pasture/scrubland and water body. Accuracy assessment of all classified images was performed through overall accuracy and kappa coefficient. The results indicated that the incorporation of these three different vegetation indices decrease the classification accuracy for the MLC and SVM classification. In addition, the maximum likelihood classification slightly outperformed the support vector machine classification approach in both overall accuracy and kappa statistics.

[MicroRNA Target Prediction Based on Support Vector Machine Ensemble Classification Algorithm of Under-sampling Technique].

PubMed

Chen, Zhiru; Hong, Wenxue

2016-02-01

Considering the low accuracy of prediction in the positive samples and poor overall classification effects caused by unbalanced sample data of MicroRNA (miRNA) target, we proposes a support vector machine (SVM)-integration of under-sampling and weight (IUSM) algorithm in this paper, an under-sampling based on the ensemble learning algorithm. The algorithm adopts SVM as learning algorithm and AdaBoost as integration framework, and embeds clustering-based under-sampling into the iterative process, aiming at reducing the degree of unbalanced distribution of positive and negative samples. Meanwhile, in the process of adaptive weight adjustment of the samples, the SVM-IUSM algorithm eliminates the abnormal ones in negative samples with robust sample weights smoothing mechanism so as to avoid over-learning. Finally, the prediction of miRNA target integrated classifier is achieved with the combination of multiple weak classifiers through the voting mechanism. The experiment revealed that the SVM-IUSW, compared with other algorithms on unbalanced dataset collection, could not only improve the accuracy of positive targets and the overall effect of classification, but also enhance the generalization ability of miRNA target classifier.

Support Vector Machine Classification of Major Depressive Disorder Using Diffusion-Weighted Neuroimaging and Graph Theory

PubMed Central

Sacchet, Matthew D.; Prasad, Gautam; Foland-Ross, Lara C.; Thompson, Paul M.; Gotlib, Ian H.

2015-01-01

Recently, there has been considerable interest in understanding brain networks in major depressive disorder (MDD). Neural pathways can be tracked in the living brain using diffusion-weighted imaging (DWI); graph theory can then be used to study properties of the resulting fiber networks. To date, global abnormalities have not been reported in tractography-based graph metrics in MDD, so we used a machine learning approach based on “support vector machines” to differentiate depressed from healthy individuals based on multiple brain network properties. We also assessed how important specific graph metrics were for this differentiation. Finally, we conducted a local graph analysis to identify abnormal connectivity at specific nodes of the network. We were able to classify depression using whole-brain graph metrics. Small-worldness was the most useful graph metric for classification. The right pars orbitalis, right inferior parietal cortex, and left rostral anterior cingulate all showed abnormal network connectivity in MDD. This is the first use of structural global graph metrics to classify depressed individuals. These findings highlight the importance of future research to understand network properties in depression across imaging modalities, improve classification results, and relate network alterations to psychiatric symptoms, medication, and comorbidities. PMID:25762941

Classifying Physical Morphology of Cocoa Beans Digital Images using Multiclass Ensemble Least-Squares Support Vector Machine

NASA Astrophysics Data System (ADS)

Lawi, Armin; Adhitya, Yudhi

2018-03-01

The objective of this research is to determine the quality of cocoa beans through morphology of their digital images. Samples of cocoa beans were scattered on a bright white paper under a controlled lighting condition. A compact digital camera was used to capture the images. The images were then processed to extract their morphological parameters. Classification process begins with an analysis of cocoa beans image based on morphological feature extraction. Parameters for extraction of morphological or physical feature parameters, i.e., Area, Perimeter, Major Axis Length, Minor Axis Length, Aspect Ratio, Circularity, Roundness, Ferret Diameter. The cocoa beans are classified into 4 groups, i.e.: Normal Beans, Broken Beans, Fractured Beans, and Skin Damaged Beans. The model of classification used in this paper is the Multiclass Ensemble Least-Squares Support Vector Machine (MELS-SVM), a proposed improvement model of SVM using ensemble method in which the separate hyperplanes are obtained by least square approach and the multiclass procedure uses One-Against- All method. The result of our proposed model showed that the classification with morphological feature input parameters were accurately as 99.705% for the four classes, respectively.

Stochastic subset selection for learning with kernel machines.

PubMed

Rhinelander, Jason; Liu, Xiaoping P

2012-06-01

Kernel machines have gained much popularity in applications of machine learning. Support vector machines (SVMs) are a subset of kernel machines and generalize well for classification, regression, and anomaly detection tasks. The training procedure for traditional SVMs involves solving a quadratic programming (QP) problem. The QP problem scales super linearly in computational effort with the number of training samples and is often used for the offline batch processing of data. Kernel machines operate by retaining a subset of observed data during training. The data vectors contained within this subset are referred to as support vectors (SVs). The work presented in this paper introduces a subset selection method for the use of kernel machines in online, changing environments. Our algorithm works by using a stochastic indexing technique when selecting a subset of SVs when computing the kernel expansion. The work described here is novel because it separates the selection of kernel basis functions from the training algorithm used. The subset selection algorithm presented here can be used in conjunction with any online training technique. It is important for online kernel machines to be computationally efficient due to the real-time requirements of online environments. Our algorithm is an important contribution because it scales linearly with the number of training samples and is compatible with current training techniques. Our algorithm outperforms standard techniques in terms of computational efficiency and provides increased recognition accuracy in our experiments. We provide results from experiments using both simulated and real-world data sets to verify our algorithm.

Differentiation of Enhancing Glioma and Primary Central Nervous System Lymphoma by Texture-Based Machine Learning.

PubMed

Alcaide-Leon, P; Dufort, P; Geraldo, A F; Alshafai, L; Maralani, P J; Spears, J; Bharatha, A

2017-06-01

Accurate preoperative differentiation of primary central nervous system lymphoma and enhancing glioma is essential to avoid unnecessary neurosurgical resection in patients with primary central nervous system lymphoma. The purpose of the study was to evaluate the diagnostic performance of a machine-learning algorithm by using texture analysis of contrast-enhanced T1-weighted images for differentiation of primary central nervous system lymphoma and enhancing glioma. Seventy-one adult patients with enhancing gliomas and 35 adult patients with primary central nervous system lymphomas were included. The tumors were manually contoured on contrast-enhanced T1WI, and the resulting volumes of interest were mined for textural features and subjected to a support vector machine-based machine-learning protocol. Three readers classified the tumors independently on contrast-enhanced T1WI. Areas under the receiver operating characteristic curves were estimated for each reader and for the support vector machine classifier. A noninferiority test for diagnostic accuracy based on paired areas under the receiver operating characteristic curve was performed with a noninferiority margin of 0.15. The mean areas under the receiver operating characteristic curve were 0.877 (95% CI, 0.798-0.955) for the support vector machine classifier; 0.878 (95% CI, 0.807-0.949) for reader 1; 0.899 (95% CI, 0.833-0.966) for reader 2; and 0.845 (95% CI, 0.757-0.933) for reader 3. The mean area under the receiver operating characteristic curve of the support vector machine classifier was significantly noninferior to the mean area under the curve of reader 1 ( P = .021), reader 2 ( P = .035), and reader 3 ( P = .007). Support vector machine classification based on textural features of contrast-enhanced T1WI is noninferior to expert human evaluation in the differentiation of primary central nervous system lymphoma and enhancing glioma. © 2017 by American Journal of Neuroradiology.

Machine learning approach for automated screening of malaria parasite using light microscopic images.

PubMed

Das, Dev Kumar; Ghosh, Madhumala; Pal, Mallika; Maiti, Asok K; Chakraborty, Chandan

2013-02-01

The aim of this paper is to address the development of computer assisted malaria parasite characterization and classification using machine learning approach based on light microscopic images of peripheral blood smears. In doing this, microscopic image acquisition from stained slides, illumination correction and noise reduction, erythrocyte segmentation, feature extraction, feature selection and finally classification of different stages of malaria (Plasmodium vivax and Plasmodium falciparum) have been investigated. The erythrocytes are segmented using marker controlled watershed transformation and subsequently total ninety six features describing shape-size and texture of erythrocytes are extracted in respect to the parasitemia infected versus non-infected cells. Ninety four features are found to be statistically significant in discriminating six classes. Here a feature selection-cum-classification scheme has been devised by combining F-statistic, statistical learning techniques i.e., Bayesian learning and support vector machine (SVM) in order to provide the higher classification accuracy using best set of discriminating features. Results show that Bayesian approach provides the highest accuracy i.e., 84% for malaria classification by selecting 19 most significant features while SVM provides highest accuracy i.e., 83.5% with 9 most significant features. Finally, the performance of these two classifiers under feature selection framework has been compared toward malaria parasite classification. Copyright © 2012 Elsevier Ltd. All rights reserved.

Exploiting machine learning algorithms for tree species classification in a semiarid woodland using RapidEye image

NASA Astrophysics Data System (ADS)

Adelabu, Samuel; Mutanga, Onisimo; Adam, Elhadi; Cho, Moses Azong

2013-01-01

Classification of different tree species in semiarid areas can be challenging as a result of the change in leaf structure and orientation due to soil moisture constraints. Tree species mapping is, however, a key parameter for forest management in semiarid environments. In this study, we examined the suitability of 5-band RapidEye satellite data for the classification of five tree species in mopane woodland of Botswana using machine leaning algorithms with limited training samples.We performed classification using random forest (RF) and support vector machines (SVM) based on EnMap box. The overall accuracies for classifying the five tree species was 88.75 and 85% for both SVM and RF, respectively. We also demonstrated that the new red-edge band in the RapidEye sensor has the potential for classifying tree species in semiarid environments when integrated with other standard bands. Similarly, we observed that where there are limited training samples, SVM is preferred over RF. Finally, we demonstrated that the two accuracy measures of quantity and allocation disagreement are simpler and more helpful for the vast majority of remote sensing classification process than the kappa coefficient. Overall, high species classification can be achieved using strategically located RapidEye bands integrated with advanced processing algorithms.

Computational approaches for the classification of seed storage proteins.

PubMed

Radhika, V; Rao, V Sree Hari

2015-07-01

Seed storage proteins comprise a major part of the protein content of the seed and have an important role on the quality of the seed. These storage proteins are important because they determine the total protein content and have an effect on the nutritional quality and functional properties for food processing. Transgenic plants are being used to develop improved lines for incorporation into plant breeding programs and the nutrient composition of seeds is a major target of molecular breeding programs. Hence, classification of these proteins is crucial for the development of superior varieties with improved nutritional quality. In this study we have applied machine learning algorithms for classification of seed storage proteins. We have presented an algorithm based on nearest neighbor approach for classification of seed storage proteins and compared its performance with decision tree J48, multilayer perceptron neural (MLP) network and support vector machine (SVM) libSVM. The model based on our algorithm has been able to give higher classification accuracy in comparison to the other methods.

Epileptic seizure detection in EEG signal using machine learning techniques.

PubMed

Jaiswal, Abeg Kumar; Banka, Haider

2018-03-01

Epilepsy is a well-known nervous system disorder characterized by seizures. Electroencephalograms (EEGs), which capture brain neural activity, can detect epilepsy. Traditional methods for analyzing an EEG signal for epileptic seizure detection are time-consuming. Recently, several automated seizure detection frameworks using machine learning technique have been proposed to replace these traditional methods. The two basic steps involved in machine learning are feature extraction and classification. Feature extraction reduces the input pattern space by keeping informative features and the classifier assigns the appropriate class label. In this paper, we propose two effective approaches involving subpattern based PCA (SpPCA) and cross-subpattern correlation-based PCA (SubXPCA) with Support Vector Machine (SVM) for automated seizure detection in EEG signals. Feature extraction was performed using SpPCA and SubXPCA. Both techniques explore the subpattern correlation of EEG signals, which helps in decision-making process. SVM is used for classification of seizure and non-seizure EEG signals. The SVM was trained with radial basis kernel. All the experiments have been carried out on the benchmark epilepsy EEG dataset. The entire dataset consists of 500 EEG signals recorded under different scenarios. Seven different experimental cases for classification have been conducted. The classification accuracy was evaluated using tenfold cross validation. The classification results of the proposed approaches have been compared with the results of some of existing techniques proposed in the literature to establish the claim.

Classification of Regional Ionospheric Disturbances Based on Support Vector Machines

NASA Astrophysics Data System (ADS)

Begüm Terzi, Merve; Arikan, Feza; Arikan, Orhan; Karatay, Secil

2016-07-01

Ionosphere is an anisotropic, inhomogeneous, time varying and spatio-temporally dispersive medium whose parameters can be estimated almost always by using indirect measurements. Geomagnetic, gravitational, solar or seismic activities cause variations of ionosphere at various spatial and temporal scales. This complex spatio-temporal variability is challenging to be identified due to extensive scales in period, duration, amplitude and frequency of disturbances. Since geomagnetic and solar indices such as Disturbance storm time (Dst), F10.7 solar flux, Sun Spot Number (SSN), Auroral Electrojet (AE), Kp and W-index provide information about variability on a global scale, identification and classification of regional disturbances poses a challenge. The main aim of this study is to classify the regional effects of global geomagnetic storms and classify them according to their risk levels. For this purpose, Total Electron Content (TEC) estimated from GPS receivers, which is one of the major parameters of ionosphere, will be used to model the regional and local variability that differs from global activity along with solar and geomagnetic indices. In this work, for the automated classification of the regional disturbances, a classification technique based on a robust machine learning technique that have found wide spread use, Support Vector Machine (SVM) is proposed. SVM is a supervised learning model used for classification with associated learning algorithm that analyze the data and recognize patterns. In addition to performing linear classification, SVM can efficiently perform nonlinear classification by embedding data into higher dimensional feature spaces. Performance of the developed classification technique is demonstrated for midlatitude ionosphere over Anatolia using TEC estimates generated from the GPS data provided by Turkish National Permanent GPS Network (TNPGN-Active) for solar maximum year of 2011. As a result of implementing the developed classification technique to the Global Ionospheric Map (GIM) TEC data which is provided by the NASA Jet Propulsion Laboratory (JPL), it will be shown that SVM can be a suitable learning method to detect the anomalies in Total Electron Content (TEC) variations. This study is supported by TUBITAK 114E541 project as a part of the Scientific and Technological Research Projects Funding Program (1001).

About decomposition approach for solving the classification problem

NASA Astrophysics Data System (ADS)

Andrianova, A. A.

2016-11-01

This article describes the features of the application of an algorithm with using of decomposition methods for solving the binary classification problem of constructing a linear classifier based on Support Vector Machine method. Application of decomposition reduces the volume of calculations, in particular, due to the emerging possibilities to build parallel versions of the algorithm, which is a very important advantage for the solution of problems with big data. The analysis of the results of computational experiments conducted using the decomposition approach. The experiment use known data set for binary classification problem.

A machine learning approach for viral genome classification.

PubMed

Remita, Mohamed Amine; Halioui, Ahmed; Malick Diouara, Abou Abdallah; Daigle, Bruno; Kiani, Golrokh; Diallo, Abdoulaye Baniré

2017-04-11

Advances in cloning and sequencing technology are yielding a massive number of viral genomes. The classification and annotation of these genomes constitute important assets in the discovery of genomic variability, taxonomic characteristics and disease mechanisms. Existing classification methods are often designed for specific well-studied family of viruses. Thus, the viral comparative genomic studies could benefit from more generic, fast and accurate tools for classifying and typing newly sequenced strains of diverse virus families. Here, we introduce a virus classification platform, CASTOR, based on machine learning methods. CASTOR is inspired by a well-known technique in molecular biology: restriction fragment length polymorphism (RFLP). It simulates, in silico, the restriction digestion of genomic material by different enzymes into fragments. It uses two metrics to construct feature vectors for machine learning algorithms in the classification step. We benchmark CASTOR for the classification of distinct datasets of human papillomaviruses (HPV), hepatitis B viruses (HBV) and human immunodeficiency viruses type 1 (HIV-1). Results reveal true positive rates of 99%, 99% and 98% for HPV Alpha species, HBV genotyping and HIV-1 M subtyping, respectively. Furthermore, CASTOR shows a competitive performance compared to well-known HIV-1 specific classifiers (REGA and COMET) on whole genomes and pol fragments. The performance of CASTOR, its genericity and robustness could permit to perform novel and accurate large scale virus studies. The CASTOR web platform provides an open access, collaborative and reproducible machine learning classifiers. CASTOR can be accessed at http://castor.bioinfo.uqam.ca .

Improving the performance of extreme learning machine for hyperspectral image classification

NASA Astrophysics Data System (ADS)

Li, Jiaojiao; Du, Qian; Li, Wei; Li, Yunsong

2015-05-01

Extreme learning machine (ELM) and kernel ELM (KELM) can offer comparable performance as the standard powerful classifier―support vector machine (SVM), but with much lower computational cost due to extremely simple training step. However, their performance may be sensitive to several parameters, such as the number of hidden neurons. An empirical linear relationship between the number of training samples and the number of hidden neurons is proposed. Such a relationship can be easily estimated with two small training sets and extended to large training sets so as to greatly reduce computational cost. Other parameters, such as the steepness parameter in the sigmodal activation function and regularization parameter in the KELM, are also investigated. The experimental results show that classification performance is sensitive to these parameters; fortunately, simple selections will result in suboptimal performance.

Source localization in an ocean waveguide using supervised machine learning.

PubMed

Niu, Haiqiang; Reeves, Emma; Gerstoft, Peter

2017-09-01

Source localization in ocean acoustics is posed as a machine learning problem in which data-driven methods learn source ranges directly from observed acoustic data. The pressure received by a vertical linear array is preprocessed by constructing a normalized sample covariance matrix and used as the input for three machine learning methods: feed-forward neural networks (FNN), support vector machines (SVM), and random forests (RF). The range estimation problem is solved both as a classification problem and as a regression problem by these three machine learning algorithms. The results of range estimation for the Noise09 experiment are compared for FNN, SVM, RF, and conventional matched-field processing and demonstrate the potential of machine learning for underwater source localization.

Comparison of the MPP with other supercomputers for LANDSAT data processing

NASA Technical Reports Server (NTRS)

Ozga, Martin

1987-01-01

The massively parallel processor is compared to the CRAY X-MP and the CYBER-205 for LANDSAT data processing. The maximum likelihood classification algorithm is the basis for comparison since this algorithm is simple to implement and vectorizes very well. The algorithm was implemented on all three machines and tested by classifying the same full scene of LANDSAT multispectral scan data. Timings are compared as well as features of the machines and available software.

Automatic ICD-10 multi-class classification of cause of death from plaintext autopsy reports through expert-driven feature selection.

PubMed

Mujtaba, Ghulam; Shuib, Liyana; Raj, Ram Gopal; Rajandram, Retnagowri; Shaikh, Khairunisa; Al-Garadi, Mohammed Ali

2017-01-01

Widespread implementation of electronic databases has improved the accessibility of plaintext clinical information for supplementary use. Numerous machine learning techniques, such as supervised machine learning approaches or ontology-based approaches, have been employed to obtain useful information from plaintext clinical data. This study proposes an automatic multi-class classification system to predict accident-related causes of death from plaintext autopsy reports through expert-driven feature selection with supervised automatic text classification decision models. Accident-related autopsy reports were obtained from one of the largest hospital in Kuala Lumpur. These reports belong to nine different accident-related causes of death. Master feature vector was prepared by extracting features from the collected autopsy reports by using unigram with lexical categorization. This master feature vector was used to detect cause of death [according to internal classification of disease version 10 (ICD-10) classification system] through five automated feature selection schemes, proposed expert-driven approach, five subset sizes of features, and five machine learning classifiers. Model performance was evaluated using precisionM, recallM, F-measureM, accuracy, and area under ROC curve. Four baselines were used to compare the results with the proposed system. Random forest and J48 decision models parameterized using expert-driven feature selection yielded the highest evaluation measure approaching (85% to 90%) for most metrics by using a feature subset size of 30. The proposed system also showed approximately 14% to 16% improvement in the overall accuracy compared with the existing techniques and four baselines. The proposed system is feasible and practical to use for automatic classification of ICD-10-related cause of death from autopsy reports. The proposed system assists pathologists to accurately and rapidly determine underlying cause of death based on autopsy findings. Furthermore, the proposed expert-driven feature selection approach and the findings are generally applicable to other kinds of plaintext clinical reports.

Automatic ICD-10 multi-class classification of cause of death from plaintext autopsy reports through expert-driven feature selection

PubMed Central

Mujtaba, Ghulam; Shuib, Liyana; Raj, Ram Gopal; Rajandram, Retnagowri; Shaikh, Khairunisa; Al-Garadi, Mohammed Ali

2017-01-01

Objectives Widespread implementation of electronic databases has improved the accessibility of plaintext clinical information for supplementary use. Numerous machine learning techniques, such as supervised machine learning approaches or ontology-based approaches, have been employed to obtain useful information from plaintext clinical data. This study proposes an automatic multi-class classification system to predict accident-related causes of death from plaintext autopsy reports through expert-driven feature selection with supervised automatic text classification decision models. Methods Accident-related autopsy reports were obtained from one of the largest hospital in Kuala Lumpur. These reports belong to nine different accident-related causes of death. Master feature vector was prepared by extracting features from the collected autopsy reports by using unigram with lexical categorization. This master feature vector was used to detect cause of death [according to internal classification of disease version 10 (ICD-10) classification system] through five automated feature selection schemes, proposed expert-driven approach, five subset sizes of features, and five machine learning classifiers. Model performance was evaluated using precisionM, recallM, F-measureM, accuracy, and area under ROC curve. Four baselines were used to compare the results with the proposed system. Results Random forest and J48 decision models parameterized using expert-driven feature selection yielded the highest evaluation measure approaching (85% to 90%) for most metrics by using a feature subset size of 30. The proposed system also showed approximately 14% to 16% improvement in the overall accuracy compared with the existing techniques and four baselines. Conclusion The proposed system is feasible and practical to use for automatic classification of ICD-10-related cause of death from autopsy reports. The proposed system assists pathologists to accurately and rapidly determine underlying cause of death based on autopsy findings. Furthermore, the proposed expert-driven feature selection approach and the findings are generally applicable to other kinds of plaintext clinical reports. PMID:28166263

Quantum Algorithm for K-Nearest Neighbors Classification Based on the Metric of Hamming Distance

NASA Astrophysics Data System (ADS)

Ruan, Yue; Xue, Xiling; Liu, Heng; Tan, Jianing; Li, Xi

2017-11-01

K-nearest neighbors (KNN) algorithm is a common algorithm used for classification, and also a sub-routine in various complicated machine learning tasks. In this paper, we presented a quantum algorithm (QKNN) for implementing this algorithm based on the metric of Hamming distance. We put forward a quantum circuit for computing Hamming distance between testing sample and each feature vector in the training set. Taking advantage of this method, we realized a good analog for classical KNN algorithm by setting a distance threshold value t to select k - n e a r e s t neighbors. As a result, QKNN achieves O( n 3) performance which is only relevant to the dimension of feature vectors and high classification accuracy, outperforms Llyod's algorithm (Lloyd et al. 2013) and Wiebe's algorithm (Wiebe et al. 2014).

Decision support system for diabetic retinopathy using discrete wavelet transform.

PubMed

Noronha, K; Acharya, U R; Nayak, K P; Kamath, S; Bhandary, S V

2013-03-01

Prolonged duration of the diabetes may affect the tiny blood vessels of the retina causing diabetic retinopathy. Routine eye screening of patients with diabetes helps to detect diabetic retinopathy at the early stage. It is very laborious and time-consuming for the doctors to go through many fundus images continuously. Therefore, decision support system for diabetic retinopathy detection can reduce the burden of the ophthalmologists. In this work, we have used discrete wavelet transform and support vector machine classifier for automated detection of normal and diabetic retinopathy classes. The wavelet-based decomposition was performed up to the second level, and eight energy features were extracted. Two energy features from the approximation coefficients of two levels and six energy values from the details in three orientations (horizontal, vertical and diagonal) were evaluated. These features were fed to the support vector machine classifier with various kernel functions (linear, radial basis function, polynomial of orders 2 and 3) to evaluate the highest classification accuracy. We obtained the highest average classification accuracy, sensitivity and specificity of more than 99% with support vector machine classifier (polynomial kernel of order 3) using three discrete wavelet transform features. We have also proposed an integrated index called Diabetic Retinopathy Risk Index using clinically significant wavelet energy features to identify normal and diabetic retinopathy classes using just one number. We believe that this (Diabetic Retinopathy Risk Index) can be used as an adjunct tool by the doctors during the eye screening to cross-check their diagnosis.

Machine learning algorithms for mode-of-action classification in toxicity assessment.

PubMed

Zhang, Yile; Wong, Yau Shu; Deng, Jian; Anton, Cristina; Gabos, Stephan; Zhang, Weiping; Huang, Dorothy Yu; Jin, Can

2016-01-01

Real Time Cell Analysis (RTCA) technology is used to monitor cellular changes continuously over the entire exposure period. Combining with different testing concentrations, the profiles have potential in probing the mode of action (MOA) of the testing substances. In this paper, we present machine learning approaches for MOA assessment. Computational tools based on artificial neural network (ANN) and support vector machine (SVM) are developed to analyze the time-concentration response curves (TCRCs) of human cell lines responding to tested chemicals. The techniques are capable of learning data from given TCRCs with known MOA information and then making MOA classification for the unknown toxicity. A novel data processing step based on wavelet transform is introduced to extract important features from the original TCRC data. From the dose response curves, time interval leading to higher classification success rate can be selected as input to enhance the performance of the machine learning algorithm. This is particularly helpful when handling cases with limited and imbalanced data. The validation of the proposed method is demonstrated by the supervised learning algorithm applied to the exposure data of HepG2 cell line to 63 chemicals with 11 concentrations in each test case. Classification success rate in the range of 85 to 95 % are obtained using SVM for MOA classification with two clusters to cases up to four clusters. Wavelet transform is capable of capturing important features of TCRCs for MOA classification. The proposed SVM scheme incorporated with wavelet transform has a great potential for large scale MOA classification and high-through output chemical screening.

Improved Hierarchical Optimization-Based Classification of Hyperspectral Images Using Shape Analysis

NASA Technical Reports Server (NTRS)

Tarabalka, Yuliya; Tilton, James C.

2012-01-01

A new spectral-spatial method for classification of hyperspectral images is proposed. The HSegClas method is based on the integration of probabilistic classification and shape analysis within the hierarchical step-wise optimization algorithm. First, probabilistic support vector machines classification is applied. Then, at each iteration two neighboring regions with the smallest Dissimilarity Criterion (DC) are merged, and classification probabilities are recomputed. The important contribution of this work consists in estimating a DC between regions as a function of statistical, classification and geometrical (area and rectangularity) features. Experimental results are presented on a 102-band ROSIS image of the Center of Pavia, Italy. The developed approach yields more accurate classification results when compared to previously proposed methods.

An ultra low power feature extraction and classification system for wearable seizure detection.

PubMed

Page, Adam; Pramod Tim Oates, Siddharth; Mohsenin, Tinoosh

2015-01-01

In this paper we explore the use of a variety of machine learning algorithms for designing a reliable and low-power, multi-channel EEG feature extractor and classifier for predicting seizures from electroencephalographic data (scalp EEG). Different machine learning classifiers including k-nearest neighbor, support vector machines, naïve Bayes, logistic regression, and neural networks are explored with the goal of maximizing detection accuracy while minimizing power, area, and latency. The input to each machine learning classifier is a 198 feature vector containing 9 features for each of the 22 EEG channels obtained over 1-second windows. All classifiers were able to obtain F1 scores over 80% and onset sensitivity of 100% when tested on 10 patients. Among five different classifiers that were explored, logistic regression (LR) proved to have minimum hardware complexity while providing average F-1 score of 91%. Both ASIC and FPGA implementations of logistic regression are presented and show the smallest area, power consumption, and the lowest latency when compared to the previous work.

Recognition and Classification of Road Condition on the Basis of Friction Force by Using a Mobile Robot

NASA Astrophysics Data System (ADS)

Watanabe, Tatsuhito; Katsura, Seiichiro

A person operating a mobile robot in a remote environment receives realistic visual feedback about the condition of the road on which the robot is moving. The categorization of the road condition is necessary to evaluate the conditions for safe and comfortable driving. For this purpose, the mobile robot should be capable of recognizing and classifying the condition of the road surfaces. This paper proposes a method for recognizing the type of road surfaces on the basis of the friction between the mobile robot and the road surfaces. This friction is estimated by a disturbance observer, and a support vector machine is used to classify the surfaces. The support vector machine identifies the type of the road surface using feature vector, which is determined using the arithmetic average and variance derived from the torque values. Further, these feature vectors are mapped onto a higher dimensional space by using a kernel function. The validity of the proposed method is confirmed by experimental results.

On the classification techniques in data mining for microarray data classification

NASA Astrophysics Data System (ADS)

Aydadenta, Husna; Adiwijaya

2018-03-01

Cancer is one of the deadly diseases, according to data from WHO by 2015 there are 8.8 million more deaths caused by cancer, and this will increase every year if not resolved earlier. Microarray data has become one of the most popular cancer-identification studies in the field of health, since microarray data can be used to look at levels of gene expression in certain cell samples that serve to analyze thousands of genes simultaneously. By using data mining technique, we can classify the sample of microarray data thus it can be identified with cancer or not. In this paper we will discuss some research using some data mining techniques using microarray data, such as Support Vector Machine (SVM), Artificial Neural Network (ANN), Naive Bayes, k-Nearest Neighbor (kNN), and C4.5, and simulation of Random Forest algorithm with technique of reduction dimension using Relief. The result of this paper show performance measure (accuracy) from classification algorithm (SVM, ANN, Naive Bayes, kNN, C4.5, and Random Forets).The results in this paper show the accuracy of Random Forest algorithm higher than other classification algorithms (Support Vector Machine (SVM), Artificial Neural Network (ANN), Naive Bayes, k-Nearest Neighbor (kNN), and C4.5). It is hoped that this paper can provide some information about the speed, accuracy, performance and computational cost generated from each Data Mining Classification Technique based on microarray data.

Automatic classification of 6-month-old infants at familial risk for language-based learning disorder using a support vector machine.

PubMed

Zare, Marzieh; Rezvani, Zahra; Benasich, April A

2016-07-01

This study assesses the ability of a novel, "automatic classification" approach to facilitate identification of infants at highest familial risk for language-learning disorders (LLD) and to provide converging assessments to enable earlier detection of developmental disorders that disrupt language acquisition. Network connectivity measures derived from 62-channel electroencephalogram (EEG) recording were used to identify selected features within two infant groups who differed on LLD risk: infants with a family history of LLD (FH+) and typically-developing infants without such a history (FH-). A support vector machine was deployed; global efficiency and global and local clustering coefficients were computed. A novel minimum spanning tree (MST) approach was also applied. Cross-validation was employed to assess the resultant classification. Infants were classified with about 80% accuracy into FH+ and FH- groups with 89% specificity and precision of 92%. Clustering patterns differed by risk group and MST network analysis suggests that FH+ infants' EEG complexity patterns were significantly different from FH- infants. The automatic classification techniques used here were shown to be both robust and reliable and should provide valuable information when applied to early identification of risk or clinical groups. The ability to identify infants at highest risk for LLD using "automatic classification" strategies is a novel convergent approach that may facilitate earlier diagnosis and remediation. Copyright © 2016 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved.

Comparative Performance Analysis of Support Vector Machine, Random Forest, Logistic Regression and k-Nearest Neighbours in Rainbow Trout (Oncorhynchus Mykiss) Classification Using Image-Based Features

PubMed Central

Císař, Petr; Labbé, Laurent; Souček, Pavel; Pelissier, Pablo; Kerneis, Thierry

2018-01-01

The main aim of this study was to develop a new objective method for evaluating the impacts of different diets on the live fish skin using image-based features. In total, one-hundred and sixty rainbow trout (Oncorhynchus mykiss) were fed either a fish-meal based diet (80 fish) or a 100% plant-based diet (80 fish) and photographed using consumer-grade digital camera. Twenty-three colour features and four texture features were extracted. Four different classification methods were used to evaluate fish diets including Random forest (RF), Support vector machine (SVM), Logistic regression (LR) and k-Nearest neighbours (k-NN). The SVM with radial based kernel provided the best classifier with correct classification rate (CCR) of 82% and Kappa coefficient of 0.65. Although the both LR and RF methods were less accurate than SVM, they achieved good classification with CCR 75% and 70% respectively. The k-NN was the least accurate (40%) classification model. Overall, it can be concluded that consumer-grade digital cameras could be employed as the fast, accurate and non-invasive sensor for classifying rainbow trout based on their diets. Furthermore, these was a close association between image-based features and fish diet received during cultivation. These procedures can be used as non-invasive, accurate and precise approaches for monitoring fish status during the cultivation by evaluating diet’s effects on fish skin. PMID:29596375

Comparative Performance Analysis of Support Vector Machine, Random Forest, Logistic Regression and k-Nearest Neighbours in Rainbow Trout (Oncorhynchus Mykiss) Classification Using Image-Based Features.

PubMed

Saberioon, Mohammadmehdi; Císař, Petr; Labbé, Laurent; Souček, Pavel; Pelissier, Pablo; Kerneis, Thierry

2018-03-29

The main aim of this study was to develop a new objective method for evaluating the impacts of different diets on the live fish skin using image-based features. In total, one-hundred and sixty rainbow trout ( Oncorhynchus mykiss ) were fed either a fish-meal based diet (80 fish) or a 100% plant-based diet (80 fish) and photographed using consumer-grade digital camera. Twenty-three colour features and four texture features were extracted. Four different classification methods were used to evaluate fish diets including Random forest (RF), Support vector machine (SVM), Logistic regression (LR) and k -Nearest neighbours ( k -NN). The SVM with radial based kernel provided the best classifier with correct classification rate (CCR) of 82% and Kappa coefficient of 0.65. Although the both LR and RF methods were less accurate than SVM, they achieved good classification with CCR 75% and 70% respectively. The k -NN was the least accurate (40%) classification model. Overall, it can be concluded that consumer-grade digital cameras could be employed as the fast, accurate and non-invasive sensor for classifying rainbow trout based on their diets. Furthermore, these was a close association between image-based features and fish diet received during cultivation. These procedures can be used as non-invasive, accurate and precise approaches for monitoring fish status during the cultivation by evaluating diet's effects on fish skin.

SVM-based feature extraction and classification of aflatoxin contaminated corn using fluorescence hyperspectral data

USDA-ARS?s Scientific Manuscript database

Support Vector Machine (SVM) was used in the Genetic Algorithms (GA) process to select and classify a subset of hyperspectral image bands. The method was applied to fluorescence hyperspectral data for the detection of aflatoxin contamination in Aspergillus flavus infected single corn kernels. In the...

Prominent feature extraction for review analysis: an empirical study

NASA Astrophysics Data System (ADS)

Agarwal, Basant; Mittal, Namita

2016-05-01

Sentiment analysis (SA) research has increased tremendously in recent times. SA aims to determine the sentiment orientation of a given text into positive or negative polarity. Motivation for SA research is the need for the industry to know the opinion of the users about their product from online portals, blogs, discussion boards and reviews and so on. Efficient features need to be extracted for machine-learning algorithm for better sentiment classification. In this paper, initially various features are extracted such as unigrams, bi-grams and dependency features from the text. In addition, new bi-tagged features are also extracted that conform to predefined part-of-speech patterns. Furthermore, various composite features are created using these features. Information gain (IG) and minimum redundancy maximum relevancy (mRMR) feature selection methods are used to eliminate the noisy and irrelevant features from the feature vector. Finally, machine-learning algorithms are used for classifying the review document into positive or negative class. Effects of different categories of features are investigated on four standard data-sets, namely, movie review and product (book, DVD and electronics) review data-sets. Experimental results show that composite features created from prominent features of unigram and bi-tagged features perform better than other features for sentiment classification. mRMR is a better feature selection method as compared with IG for sentiment classification. Boolean Multinomial Naïve Bayes) algorithm performs better than support vector machine classifier for SA in terms of accuracy and execution time.

Feature generation using genetic programming with application to fault classification.

PubMed

Guo, Hong; Jack, Lindsay B; Nandi, Asoke K

2005-02-01

One of the major challenges in pattern recognition problems is the feature extraction process which derives new features from existing features, or directly from raw data in order to reduce the cost of computation during the classification process, while improving classifier efficiency. Most current feature extraction techniques transform the original pattern vector into a new vector with increased discrimination capability but lower dimensionality. This is conducted within a predefined feature space, and thus, has limited searching power. Genetic programming (GP) can generate new features from the original dataset without prior knowledge of the probabilistic distribution. In this paper, a GP-based approach is developed for feature extraction from raw vibration data recorded from a rotating machine with six different conditions. The created features are then used as the inputs to a neural classifier for the identification of six bearing conditions. Experimental results demonstrate the ability of GP to discover autimatically the different bearing conditions using features expressed in the form of nonlinear functions. Furthermore, four sets of results--using GP extracted features with artificial neural networks (ANN) and support vector machines (SVM), as well as traditional features with ANN and SVM--have been obtained. This GP-based approach is used for bearing fault classification for the first time and exhibits superior searching power over other techniques. Additionaly, it significantly reduces the time for computation compared with genetic algorithm (GA), therefore, makes a more practical realization of the solution.

Relevance Vector Machine and Support Vector Machine Classifier Analysis of Scanning Laser Polarimetry Retinal Nerve Fiber Layer Measurements

PubMed Central

Bowd, Christopher; Medeiros, Felipe A.; Zhang, Zuohua; Zangwill, Linda M.; Hao, Jiucang; Lee, Te-Won; Sejnowski, Terrence J.; Weinreb, Robert N.; Goldbaum, Michael H.

2010-01-01

Purpose To classify healthy and glaucomatous eyes using relevance vector machine (RVM) and support vector machine (SVM) learning classifiers trained on retinal nerve fiber layer (RNFL) thickness measurements obtained by scanning laser polarimetry (SLP). Methods Seventy-two eyes of 72 healthy control subjects (average age = 64.3 ± 8.8 years, visual field mean deviation =−0.71 ± 1.2 dB) and 92 eyes of 92 patients with glaucoma (average age = 66.9 ± 8.9 years, visual field mean deviation =−5.32 ± 4.0 dB) were imaged with SLP with variable corneal compensation (GDx VCC; Laser Diagnostic Technologies, San Diego, CA). RVM and SVM learning classifiers were trained and tested on SLP-determined RNFL thickness measurements from 14 standard parameters and 64 sectors (approximately 5.6° each) obtained in the circumpapillary area under the instrument-defined measurement ellipse (total 78 parameters). Tenfold cross-validation was used to train and test RVM and SVM classifiers on unique subsets of the full 164-eye data set and areas under the receiver operating characteristic (AUROC) curve for the classification of eyes in the test set were generated. AUROC curve results from RVM and SVM were compared to those for 14 SLP software-generated global and regional RNFL thickness parameters. Also reported was the AUROC curve for the GDx VCC software-generated nerve fiber indicator (NFI). Results The AUROC curves for RVM and SVM were 0.90 and 0.91, respectively, and increased to 0.93 and 0.94 when the training sets were optimized with sequential forward and backward selection (resulting in reduced dimensional data sets). AUROC curves for optimized RVM and SVM were significantly larger than those for all individual SLP parameters. The AUROC curve for the NFI was 0.87. Conclusions Results from RVM and SVM trained on SLP RNFL thickness measurements are similar and provide accurate classification of glaucomatous and healthy eyes. RVM may be preferable to SVM, because it provides a Bayesian-derived probability of glaucoma as an output. These results suggest that these machine learning classifiers show good potential for glaucoma diagnosis. PMID:15790898

Computer-Aided Diagnosis for Breast Ultrasound Using Computerized BI-RADS Features and Machine Learning Methods.

PubMed

Shan, Juan; Alam, S Kaisar; Garra, Brian; Zhang, Yingtao; Ahmed, Tahira

2016-04-01

This work identifies effective computable features from the Breast Imaging Reporting and Data System (BI-RADS), to develop a computer-aided diagnosis (CAD) system for breast ultrasound. Computerized features corresponding to ultrasound BI-RADs categories were designed and tested using a database of 283 pathology-proven benign and malignant lesions. Features were selected based on classification performance using a "bottom-up" approach for different machine learning methods, including decision tree, artificial neural network, random forest and support vector machine. Using 10-fold cross-validation on the database of 283 cases, the highest area under the receiver operating characteristic (ROC) curve (AUC) was 0.84 from a support vector machine with 77.7% overall accuracy; the highest overall accuracy, 78.5%, was from a random forest with the AUC 0.83. Lesion margin and orientation were optimum features common to all of the different machine learning methods. These features can be used in CAD systems to help distinguish benign from worrisome lesions. Copyright © 2016 World Federation for Ultrasound in Medicine & Biology. All rights reserved.

Design of an Adaptive Human-Machine System Based on Dynamical Pattern Recognition of Cognitive Task-Load.

PubMed

Zhang, Jianhua; Yin, Zhong; Wang, Rubin

2017-01-01

This paper developed a cognitive task-load (CTL) classification algorithm and allocation strategy to sustain the optimal operator CTL levels over time in safety-critical human-machine integrated systems. An adaptive human-machine system is designed based on a non-linear dynamic CTL classifier, which maps a set of electroencephalogram (EEG) and electrocardiogram (ECG) related features to a few CTL classes. The least-squares support vector machine (LSSVM) is used as dynamic pattern classifier. A series of electrophysiological and performance data acquisition experiments were performed on seven volunteer participants under a simulated process control task environment. The participant-specific dynamic LSSVM model is constructed to classify the instantaneous CTL into five classes at each time instant. The initial feature set, comprising 56 EEG and ECG related features, is reduced to a set of 12 salient features (including 11 EEG-related features) by using the locality preserving projection (LPP) technique. An overall correct classification rate of about 80% is achieved for the 5-class CTL classification problem. Then the predicted CTL is used to adaptively allocate the number of process control tasks between operator and computer-based controller. Simulation results showed that the overall performance of the human-machine system can be improved by using the adaptive automation strategy proposed.

Quad-polarized synthetic aperture radar and multispectral data classification using classification and regression tree and support vector machine-based data fusion system

NASA Astrophysics Data System (ADS)

Bigdeli, Behnaz; Pahlavani, Parham

2017-01-01

Interpretation of synthetic aperture radar (SAR) data processing is difficult because the geometry and spectral range of SAR are different from optical imagery. Consequently, SAR imaging can be a complementary data to multispectral (MS) optical remote sensing techniques because it does not depend on solar illumination and weather conditions. This study presents a multisensor fusion of SAR and MS data based on the use of classification and regression tree (CART) and support vector machine (SVM) through a decision fusion system. First, different feature extraction strategies were applied on SAR and MS data to produce more spectral and textural information. To overcome the redundancy and correlation between features, an intrinsic dimension estimation method based on noise-whitened Harsanyi, Farrand, and Chang determines the proper dimension of the features. Then, principal component analysis and independent component analysis were utilized on stacked feature space of two data. Afterward, SVM and CART classified each reduced feature space. Finally, a fusion strategy was utilized to fuse the classification results. To show the effectiveness of the proposed methodology, single classification on each data was compared to the obtained results. A coregistered Radarsat-2 and WorldView-2 data set from San Francisco, USA, was available to examine the effectiveness of the proposed method. The results show that combinations of SAR data with optical sensor based on the proposed methodology improve the classification results for most of the classes. The proposed fusion method provided approximately 93.24% and 95.44% for two different areas of the data.

Extraction and Analysis of Mega Cities’ Impervious Surface on Pixel-based and Object-oriented Support Vector Machine Classification Technology: A case of Bombay

NASA Astrophysics Data System (ADS)

Yu, S. S.; Sun, Z. C.; Sun, L.; Wu, M. F.

2017-02-01

The object of this paper is to study the impervious surface extraction method using remote sensing imagery and monitor the spatiotemporal changing patterns of mega cities. Megacity Bombay was selected as the interesting area. Firstly, the pixel-based and object-oriented support vector machine (SVM) classification methods were used to acquire the land use/land cover (LULC) products of Bombay in 2010. Consequently, the overall accuracy (OA) and overall Kappa (OK) of the pixel-based method were 94.97% and 0.96 with a running time of 78 minutes, the OA and OK of the object-oriented method were 93.72% and 0.94 with a running time of only 17s. Additionally, OA and OK of the object-oriented method after a post-classification were improved up to 95.8% and 0.94. Then, the dynamic impervious surfaces of Bombay in the period 1973-2015 were extracted and the urbanization pattern of Bombay was analysed. Results told that both the two SVM classification methods could accomplish the impervious surface extraction, but the object-oriented method should be a better choice. Urbanization of Bombay experienced a fast extending during the past 42 years, implying a dramatically urban sprawl of mega cities in the developing countries along the One Belt and One Road (OBOR).

Comparative evaluation of support vector machine classification for computer aided detection of breast masses in mammography

NASA Astrophysics Data System (ADS)

Lesniak, J. M.; Hupse, R.; Blanc, R.; Karssemeijer, N.; Székely, G.

2012-08-01

False positive (FP) marks represent an obstacle for effective use of computer-aided detection (CADe) of breast masses in mammography. Typically, the problem can be approached either by developing more discriminative features or by employing different classifier designs. In this paper, the usage of support vector machine (SVM) classification for FP reduction in CADe is investigated, presenting a systematic quantitative evaluation against neural networks, k-nearest neighbor classification, linear discriminant analysis and random forests. A large database of 2516 film mammography examinations and 73 input features was used to train the classifiers and evaluate for their performance on correctly diagnosed exams as well as false negatives. Further, classifier robustness was investigated using varying training data and feature sets as input. The evaluation was based on the mean exam sensitivity in 0.05-1 FPs on normals on the free-response receiver operating characteristic curve (FROC), incorporated into a tenfold cross validation framework. It was found that SVM classification using a Gaussian kernel offered significantly increased detection performance (P = 0.0002) compared to the reference methods. Varying training data and input features, SVMs showed improved exploitation of large feature sets. It is concluded that with the SVM-based CADe a significant reduction of FPs is possible outperforming other state-of-the-art approaches for breast mass CADe.

Descriptive Statistics of the Genome: Phylogenetic Classification of Viruses.

PubMed

Hernandez, Troy; Yang, Jie

2016-10-01

The typical process for classifying and submitting a newly sequenced virus to the NCBI database involves two steps. First, a BLAST search is performed to determine likely family candidates. That is followed by checking the candidate families with the pairwise sequence alignment tool for similar species. The submitter's judgment is then used to determine the most likely species classification. The aim of this article is to show that this process can be automated into a fast, accurate, one-step process using the proposed alignment-free method and properly implemented machine learning techniques. We present a new family of alignment-free vectorizations of the genome, the generalized vector, that maintains the speed of existing alignment-free methods while outperforming all available methods. This new alignment-free vectorization uses the frequency of genomic words (k-mers), as is done in the composition vector, and incorporates descriptive statistics of those k-mers' positional information, as inspired by the natural vector. We analyze five different characterizations of genome similarity using k-nearest neighbor classification and evaluate these on two collections of viruses totaling over 10,000 viruses. We show that our proposed method performs better than, or as well as, other methods at every level of the phylogenetic hierarchy. The data and R code is available upon request.

Classification of suicide attempters in schizophrenia using sociocultural and clinical features: A machine learning approach.

PubMed

Hettige, Nuwan C; Nguyen, Thai Binh; Yuan, Chen; Rajakulendran, Thanara; Baddour, Jermeen; Bhagwat, Nikhil; Bani-Fatemi, Ali; Voineskos, Aristotle N; Mallar Chakravarty, M; De Luca, Vincenzo

2017-07-01

Suicide is a major concern for those afflicted by schizophrenia. Identifying patients at the highest risk for future suicide attempts remains a complex problem for psychiatric interventions. Machine learning models allow for the integration of many risk factors in order to build an algorithm that predicts which patients are likely to attempt suicide. Currently it is unclear how to integrate previously identified risk factors into a clinically relevant predictive tool to estimate the probability of a patient with schizophrenia for attempting suicide. We conducted a cross-sectional assessment on a sample of 345 participants diagnosed with schizophrenia spectrum disorders. Suicide attempters and non-attempters were clearly identified using the Columbia Suicide Severity Rating Scale (C-SSRS) and the Beck Suicide Ideation Scale (BSS). We developed four classification algorithms using a regularized regression, random forest, elastic net and support vector machine models with sociocultural and clinical variables as features to train the models. All classification models performed similarly in identifying suicide attempters and non-attempters. Our regularized logistic regression model demonstrated an accuracy of 67% and an area under the curve (AUC) of 0.71, while the random forest model demonstrated 66% accuracy and an AUC of 0.67. Support vector classifier (SVC) model demonstrated an accuracy of 67% and an AUC of 0.70, and the elastic net model demonstrated and accuracy of 65% and an AUC of 0.71. Machine learning algorithms offer a relatively successful method for incorporating many clinical features to predict individuals at risk for future suicide attempts. Increased performance of these models using clinically relevant variables offers the potential to facilitate early treatment and intervention to prevent future suicide attempts. Copyright © 2017 Elsevier Inc. All rights reserved.

Data-driven mapping of the potential mountain permafrost distribution.

PubMed

Deluigi, Nicola; Lambiel, Christophe; Kanevski, Mikhail

2017-07-15

Existing mountain permafrost distribution models generally offer a good overview of the potential extent of this phenomenon at a regional scale. They are however not always able to reproduce the high spatial discontinuity of permafrost at the micro-scale (scale of a specific landform; ten to several hundreds of meters). To overcome this lack, we tested an alternative modelling approach using three classification algorithms belonging to statistics and machine learning: Logistic regression, Support Vector Machines and Random forests. These supervised learning techniques infer a classification function from labelled training data (pixels of permafrost absence and presence) with the aim of predicting the permafrost occurrence where it is unknown. The research was carried out in a 588km 2 area of the Western Swiss Alps. Permafrost evidences were mapped from ortho-image interpretation (rock glacier inventorying) and field data (mainly geoelectrical and thermal data). The relationship between selected permafrost evidences and permafrost controlling factors was computed with the mentioned techniques. Classification performances, assessed with AUROC, range between 0.81 for Logistic regression, 0.85 with Support Vector Machines and 0.88 with Random forests. The adopted machine learning algorithms have demonstrated to be efficient for permafrost distribution modelling thanks to consistent results compared to the field reality. The high resolution of the input dataset (10m) allows elaborating maps at the micro-scale with a modelled permafrost spatial distribution less optimistic than classic spatial models. Moreover, the probability output of adopted algorithms offers a more precise overview of the potential distribution of mountain permafrost than proposing simple indexes of the permafrost favorability. These encouraging results also open the way to new possibilities of permafrost data analysis and mapping. Copyright © 2017 Elsevier B.V. All rights reserved.

Machine learning classification with confidence: application of transductive conformal predictors to MRI-based diagnostic and prognostic markers in depression.

PubMed

Nouretdinov, Ilia; Costafreda, Sergi G; Gammerman, Alexander; Chervonenkis, Alexey; Vovk, Vladimir; Vapnik, Vladimir; Fu, Cynthia H Y

2011-05-15

There is rapidly accumulating evidence that the application of machine learning classification to neuroimaging measurements may be valuable for the development of diagnostic and prognostic prediction tools in psychiatry. However, current methods do not produce a measure of the reliability of the predictions. Knowing the risk of the error associated with a given prediction is essential for the development of neuroimaging-based clinical tools. We propose a general probabilistic classification method to produce measures of confidence for magnetic resonance imaging (MRI) data. We describe the application of transductive conformal predictor (TCP) to MRI images. TCP generates the most likely prediction and a valid measure of confidence, as well as the set of all possible predictions for a given confidence level. We present the theoretical motivation for TCP, and we have applied TCP to structural and functional MRI data in patients and healthy controls to investigate diagnostic and prognostic prediction in depression. We verify that TCP predictions are as accurate as those obtained with more standard machine learning methods, such as support vector machine, while providing the additional benefit of a valid measure of confidence for each prediction. Copyright © 2010 Elsevier Inc. All rights reserved.

Agricultural mapping using Support Vector Machine-Based Endmember Extraction (SVM-BEE)

DOE Office of Scientific and Technical Information (OSTI.GOV)

Archibald, Richard K; Filippi, Anthony M; Bhaduri, Budhendra L

Extracting endmembers from remotely sensed images of vegetated areas can present difficulties. In this research, we applied a recently developed endmember-extraction algorithm based on Support Vector Machines (SVMs) to the problem of semi-autonomous estimation of vegetation endmembers from a hyperspectral image. This algorithm, referred to as Support Vector Machine-Based Endmember Extraction (SVM-BEE), accurately and rapidly yields a computed representation of hyperspectral data that can accommodate multiple distributions. The number of distributions is identified without prior knowledge, based upon this representation. Prior work established that SVM-BEE is robustly noise-tolerant and can semi-automatically and effectively estimate endmembers; synthetic data and a geologicmore » scene were previously analyzed. Here we compared the efficacies of the SVM-BEE and N-FINDR algorithms in extracting endmembers from a predominantly agricultural scene. SVM-BEE was able to estimate vegetation and other endmembers for all classes in the image, which N-FINDR failed to do. Classifications based on SVM-BEE endmembers were markedly more accurate compared with those based on N-FINDR endmembers.« less

Label-free sensor for automatic identification of erythrocytes using digital in-line holographic microscopy and machine learning.

PubMed

Go, Taesik; Byeon, Hyeokjun; Lee, Sang Joon

2018-04-30

Cell types of erythrocytes should be identified because they are closely related to their functionality and viability. Conventional methods for classifying erythrocytes are time consuming and labor intensive. Therefore, an automatic and accurate erythrocyte classification system is indispensable in healthcare and biomedical fields. In this study, we proposed a new label-free sensor for automatic identification of erythrocyte cell types using a digital in-line holographic microscopy (DIHM) combined with machine learning algorithms. A total of 12 features, including information on intensity distributions, morphological descriptors, and optical focusing characteristics, is quantitatively obtained from numerically reconstructed holographic images. All individual features for discocytes, echinocytes, and spherocytes are statistically different. To improve the performance of cell type identification, we adopted several machine learning algorithms, such as decision tree model, support vector machine, linear discriminant classification, and k-nearest neighbor classification. With the aid of these machine learning algorithms, the extracted features are effectively utilized to distinguish erythrocytes. Among the four tested algorithms, the decision tree model exhibits the best identification performance for the training sets (n = 440, 98.18%) and test sets (n = 190, 97.37%). This proposed methodology, which smartly combined DIHM and machine learning, would be helpful for sensing abnormal erythrocytes and computer-aided diagnosis of hematological diseases in clinic. Copyright © 2017 Elsevier B.V. All rights reserved.

Rapid authentication of adulteration of olive oil by near-infrared spectroscopy using support vector machines

NASA Astrophysics Data System (ADS)

Wu, Jingzhu; Dong, Jingjing; Dong, Wenfei; Chen, Yan; Liu, Cuiling

2016-10-01

A classification method of support vector machines with linear kernel was employed to authenticate genuine olive oil based on near-infrared spectroscopy. There were three types of adulteration of olive oil experimented in the study. The adulterated oil was respectively soybean oil, rapeseed oil and the mixture of soybean and rapeseed oil. The average recognition rate of second experiment was more than 90% and that of the third experiment was reach to 100%. The results showed the method had good performance in classifying genuine olive oil and the adulteration with small variation range of adulterated concentration and it was a promising and rapid technique for the detection of oil adulteration and fraud in the food industry.

Efficient brain lesion segmentation using multi-modality tissue-based feature selection and support vector machines.

PubMed

Fiot, Jean-Baptiste; Cohen, Laurent D; Raniga, Parnesh; Fripp, Jurgen

2013-09-01

Support vector machines (SVM) are machine learning techniques that have been used for segmentation and classification of medical images, including segmentation of white matter hyper-intensities (WMH). Current approaches using SVM for WMH segmentation extract features from the brain and classify these followed by complex post-processing steps to remove false positives. The method presented in this paper combines advanced pre-processing, tissue-based feature selection and SVM classification to obtain efficient and accurate WMH segmentation. Features from 125 patients, generated from up to four MR modalities [T1-w, T2-w, proton-density and fluid attenuated inversion recovery(FLAIR)], differing neighbourhood sizes and the use of multi-scale features were compared. We found that although using all four modalities gave the best overall classification (average Dice scores of 0.54  ±  0.12, 0.72  ±  0.06 and 0.82  ±  0.06 respectively for small, moderate and severe lesion loads); this was not significantly different (p = 0.50) from using just T1-w and FLAIR sequences (Dice scores of 0.52  ±  0.13, 0.71  ±  0.08 and 0.81  ±  0.07). Furthermore, there was a negligible difference between using 5 × 5 × 5 and 3 × 3 × 3 features (p = 0.93). Finally, we show that careful consideration of features and pre-processing techniques not only saves storage space and computation time but also leads to more efficient classification, which outperforms the one based on all features with post-processing. Copyright © 2013 John Wiley & Sons, Ltd.

Best Merge Region Growing with Integrated Probabilistic Classification for Hyperspectral Imagery

NASA Technical Reports Server (NTRS)

Tarabalka, Yuliya; Tilton, James C.

2011-01-01

A new method for spectral-spatial classification of hyperspectral images is proposed. The method is based on the integration of probabilistic classification within the hierarchical best merge region growing algorithm. For this purpose, preliminary probabilistic support vector machines classification is performed. Then, hierarchical step-wise optimization algorithm is applied, by iteratively merging regions with the smallest Dissimilarity Criterion (DC). The main novelty of this method consists in defining a DC between regions as a function of region statistical and geometrical features along with classification probabilities. Experimental results are presented on a 200-band AVIRIS image of the Northwestern Indiana s vegetation area and compared with those obtained by recently proposed spectral-spatial classification techniques. The proposed method improves classification accuracies when compared to other classification approaches.

Pattern Classification Using an Olfactory Model with PCA Feature Selection in Electronic Noses: Study and Application

PubMed Central

Fu, Jun; Huang, Canqin; Xing, Jianguo; Zheng, Junbao

2012-01-01

Biologically-inspired models and algorithms are considered as promising sensor array signal processing methods for electronic noses. Feature selection is one of the most important issues for developing robust pattern recognition models in machine learning. This paper describes an investigation into the classification performance of a bionic olfactory model with the increase of the dimensions of input feature vector (outer factor) as well as its parallel channels (inner factor). The principal component analysis technique was applied for feature selection and dimension reduction. Two data sets of three classes of wine derived from different cultivars and five classes of green tea derived from five different provinces of China were used for experiments. In the former case the results showed that the average correct classification rate increased as more principal components were put in to feature vector. In the latter case the results showed that sufficient parallel channels should be reserved in the model to avoid pattern space crowding. We concluded that 6∼8 channels of the model with principal component feature vector values of at least 90% cumulative variance is adequate for a classification task of 3∼5 pattern classes considering the trade-off between time consumption and classification rate. PMID:22736979

A Fast SVM-Based Tongue's Colour Classification Aided by k-Means Clustering Identifiers and Colour Attributes as Computer-Assisted Tool for Tongue Diagnosis.

PubMed

Kamarudin, Nur Diyana; Ooi, Chia Yee; Kawanabe, Tadaaki; Odaguchi, Hiroshi; Kobayashi, Fuminori

2017-01-01

In tongue diagnosis, colour information of tongue body has kept valuable information regarding the state of disease and its correlation with the internal organs. Qualitatively, practitioners may have difficulty in their judgement due to the instable lighting condition and naked eye's ability to capture the exact colour distribution on the tongue especially the tongue with multicolour substance. To overcome this ambiguity, this paper presents a two-stage tongue's multicolour classification based on a support vector machine (SVM) whose support vectors are reduced by our proposed k -means clustering identifiers and red colour range for precise tongue colour diagnosis. In the first stage, k -means clustering is used to cluster a tongue image into four clusters of image background (black), deep red region, red/light red region, and transitional region. In the second-stage classification, red/light red tongue images are further classified into red tongue or light red tongue based on the red colour range derived in our work. Overall, true rate classification accuracy of the proposed two-stage classification to diagnose red, light red, and deep red tongue colours is 94%. The number of support vectors in SVM is improved by 41.2%, and the execution time for one image is recorded as 48 seconds.

A Fast SVM-Based Tongue's Colour Classification Aided by k-Means Clustering Identifiers and Colour Attributes as Computer-Assisted Tool for Tongue Diagnosis

PubMed Central

Ooi, Chia Yee; Kawanabe, Tadaaki; Odaguchi, Hiroshi; Kobayashi, Fuminori

2017-01-01

In tongue diagnosis, colour information of tongue body has kept valuable information regarding the state of disease and its correlation with the internal organs. Qualitatively, practitioners may have difficulty in their judgement due to the instable lighting condition and naked eye's ability to capture the exact colour distribution on the tongue especially the tongue with multicolour substance. To overcome this ambiguity, this paper presents a two-stage tongue's multicolour classification based on a support vector machine (SVM) whose support vectors are reduced by our proposed k-means clustering identifiers and red colour range for precise tongue colour diagnosis. In the first stage, k-means clustering is used to cluster a tongue image into four clusters of image background (black), deep red region, red/light red region, and transitional region. In the second-stage classification, red/light red tongue images are further classified into red tongue or light red tongue based on the red colour range derived in our work. Overall, true rate classification accuracy of the proposed two-stage classification to diagnose red, light red, and deep red tongue colours is 94%. The number of support vectors in SVM is improved by 41.2%, and the execution time for one image is recorded as 48 seconds. PMID:29065640

Accuracy of land use change detection using support vector machine and maximum likelihood techniques for open-cast coal mining areas.

PubMed

Karan, Shivesh Kishore; Samadder, Sukha Ranjan

2016-08-01

One objective of the present study was to evaluate the performance of support vector machine (SVM)-based image classification technique with the maximum likelihood classification (MLC) technique for a rapidly changing landscape of an open-cast mine. The other objective was to assess the change in land use pattern due to coal mining from 2006 to 2016. Assessing the change in land use pattern accurately is important for the development and monitoring of coalfields in conjunction with sustainable development. For the present study, Landsat 5 Thematic Mapper (TM) data of 2006 and Landsat 8 Operational Land Imager (OLI)/Thermal Infrared Sensor (TIRS) data of 2016 of a part of Jharia Coalfield, Dhanbad, India, were used. The SVM classification technique provided greater overall classification accuracy when compared to the MLC technique in classifying heterogeneous landscape with limited training dataset. SVM exceeded MLC in handling a difficult challenge of classifying features having near similar reflectance on the mean signature plot, an improvement of over 11 % was observed in classification of built-up area, and an improvement of 24 % was observed in classification of surface water using SVM; similarly, the SVM technique improved the overall land use classification accuracy by almost 6 and 3 % for Landsat 5 and Landsat 8 images, respectively. Results indicated that land degradation increased significantly from 2006 to 2016 in the study area. This study will help in quantifying the changes and can also serve as a basis for further decision support system studies aiding a variety of purposes such as planning and management of mines and environmental impact assessment.

Machine learning on brain MRI data for differential diagnosis of Parkinson's disease and Progressive Supranuclear Palsy.

PubMed

Salvatore, C; Cerasa, A; Castiglioni, I; Gallivanone, F; Augimeri, A; Lopez, M; Arabia, G; Morelli, M; Gilardi, M C; Quattrone, A

2014-01-30

Supervised machine learning has been proposed as a revolutionary approach for identifying sensitive medical image biomarkers (or combination of them) allowing for automatic diagnosis of individual subjects. The aim of this work was to assess the feasibility of a supervised machine learning algorithm for the assisted diagnosis of patients with clinically diagnosed Parkinson's disease (PD) and Progressive Supranuclear Palsy (PSP). Morphological T1-weighted Magnetic Resonance Images (MRIs) of PD patients (28), PSP patients (28) and healthy control subjects (28) were used by a supervised machine learning algorithm based on the combination of Principal Components Analysis as feature extraction technique and on Support Vector Machines as classification algorithm. The algorithm was able to obtain voxel-based morphological biomarkers of PD and PSP. The algorithm allowed individual diagnosis of PD versus controls, PSP versus controls and PSP versus PD with an Accuracy, Specificity and Sensitivity>90%. Voxels influencing classification between PD and PSP patients involved midbrain, pons, corpus callosum and thalamus, four critical regions known to be strongly involved in the pathophysiological mechanisms of PSP. Classification accuracy of individual PSP patients was consistent with previous manual morphological metrics and with other supervised machine learning application to MRI data, whereas accuracy in the detection of individual PD patients was significantly higher with our classification method. The algorithm provides excellent discrimination of PD patients from PSP patients at an individual level, thus encouraging the application of computer-based diagnosis in clinical practice. Copyright © 2013 Elsevier B.V. All rights reserved.

Automated discrimination of dementia spectrum disorders using extreme learning machine and structural T1 MRI features.

PubMed

Jongin Kim; Boreom Lee

2017-07-01

The classification of neuroimaging data for the diagnosis of Alzheimer's Disease (AD) is one of the main research goals of the neuroscience and clinical fields. In this study, we performed extreme learning machine (ELM) classifier to discriminate the AD, mild cognitive impairment (MCI) from normal control (NC). We compared the performance of ELM with that of a linear kernel support vector machine (SVM) for 718 structural MRI images from Alzheimer's Disease Neuroimaging Initiative (ADNI) database. The data consisted of normal control, MCI converter (MCI-C), MCI non-converter (MCI-NC), and AD. We employed SVM-based recursive feature elimination (RFE-SVM) algorithm to find the optimal subset of features. In this study, we found that the RFE-SVM feature selection approach in combination with ELM shows the superior classification accuracy to that of linear kernel SVM for structural T1 MRI data.

A multilevel-ROI-features-based machine learning method for detection of morphometric biomarkers in Parkinson's disease.

PubMed

Peng, Bo; Wang, Suhong; Zhou, Zhiyong; Liu, Yan; Tong, Baotong; Zhang, Tao; Dai, Yakang

2017-06-09

Machine learning methods have been widely used in recent years for detection of neuroimaging biomarkers in regions of interest (ROIs) and assisting diagnosis of neurodegenerative diseases. The innovation of this study is to use multilevel-ROI-features-based machine learning method to detect sensitive morphometric biomarkers in Parkinson's disease (PD). Specifically, the low-level ROI features (gray matter volume, cortical thickness, etc.) and high-level correlative features (connectivity between ROIs) are integrated to construct the multilevel ROI features. Filter- and wrapper- based feature selection method and multi-kernel support vector machine (SVM) are used in the classification algorithm. T1-weighted brain magnetic resonance (MR) images of 69 PD patients and 103 normal controls from the Parkinson's Progression Markers Initiative (PPMI) dataset are included in the study. The machine learning method performs well in classification between PD patients and normal controls with an accuracy of 85.78%, a specificity of 87.79%, and a sensitivity of 87.64%. The most sensitive biomarkers between PD patients and normal controls are mainly distributed in frontal lobe, parental lobe, limbic lobe, temporal lobe, and central region. The classification performance of our method with multilevel ROI features is significantly improved comparing with other classification methods using single-level features. The proposed method shows promising identification ability for detecting morphometric biomarkers in PD, thus confirming the potentiality of our method in assisting diagnosis of the disease. Copyright © 2017 Elsevier B.V. All rights reserved.

The Identification of Hunger Behaviour of Lates Calcarifer through the Integration of Image Processing Technique and Support Vector Machine

NASA Astrophysics Data System (ADS)

Taha, Z.; Razman, M. A. M.; Adnan, F. A.; Ghani, A. S. Abdul; Majeed, A. P. P. Abdul; Musa, R. M.; Sallehudin, M. F.; Mukai, Y.

2018-03-01

Fish Hunger behaviour is one of the important element in determining the fish feeding routine, especially for farmed fishes. Inaccurate feeding routines (under-feeding or over-feeding) lead the fishes to die and thus, reduces the total production of fishes. The excessive food which is not eaten by fish will be dissolved in the water and thus, reduce the water quality (oxygen quantity in the water will be reduced). The reduction of oxygen (water quality) leads the fish to die and in some cases, may lead to fish diseases. This study correlates Barramundi fish-school behaviour with hunger condition through the hybrid data integration of image processing technique. The behaviour is clustered with respect to the position of the centre of gravity of the school of fish prior feeding, during feeding and after feeding. The clustered fish behaviour is then classified by means of a machine learning technique namely Support vector machine (SVM). It has been shown from the study that the Fine Gaussian variation of SVM is able to provide a reasonably accurate classification of fish feeding behaviour with a classification accuracy of 79.7%. The proposed integration technique may increase the usefulness of the captured data and thus better differentiates the various behaviour of farmed fishes.

Differentiation of Glioblastoma and Lymphoma Using Feature Extraction and Support Vector Machine.

PubMed

Yang, Zhangjing; Feng, Piaopiao; Wen, Tian; Wan, Minghua; Hong, Xunning

2017-01-01

Differentiation of glioblastoma multiformes (GBMs) and lymphomas using multi-sequence magnetic resonance imaging (MRI) is an important task that is valuable for treatment planning. However, this task is a challenge because GBMs and lymphomas may have a similar appearance in MRI images. This similarity may lead to misclassification and could affect the treatment results. In this paper, we propose a semi-automatic method based on multi-sequence MRI to differentiate these two types of brain tumors. Our method consists of three steps: 1) the key slice is selected from 3D MRIs and region of interests (ROIs) are drawn around the tumor region; 2) different features are extracted based on prior clinical knowledge and validated using a t-test; and 3) features that are helpful for classification are used to build an original feature vector and a support vector machine is applied to perform classification. In total, 58 GBM cases and 37 lymphoma cases are used to validate our method. A leave-one-out crossvalidation strategy is adopted in our experiments. The global accuracy of our method was determined as 96.84%, which indicates that our method is effective for the differentiation of GBM and lymphoma and can be applied in clinical diagnosis. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

Classification of EMG signals using PSO optimized SVM for diagnosis of neuromuscular disorders.

PubMed

Subasi, Abdulhamit

2013-06-01

Support vector machine (SVM) is an extensively used machine learning method with many biomedical signal classification applications. In this study, a novel PSO-SVM model has been proposed that hybridized the particle swarm optimization (PSO) and SVM to improve the EMG signal classification accuracy. This optimization mechanism involves kernel parameter setting in the SVM training procedure, which significantly influences the classification accuracy. The experiments were conducted on the basis of EMG signal to classify into normal, neurogenic or myopathic. In the proposed method the EMG signals were decomposed into the frequency sub-bands using discrete wavelet transform (DWT) and a set of statistical features were extracted from these sub-bands to represent the distribution of wavelet coefficients. The obtained results obviously validate the superiority of the SVM method compared to conventional machine learning methods, and suggest that further significant enhancements in terms of classification accuracy can be achieved by the proposed PSO-SVM classification system. The PSO-SVM yielded an overall accuracy of 97.41% on 1200 EMG signals selected from 27 subject records against 96.75%, 95.17% and 94.08% for the SVM, the k-NN and the RBF classifiers, respectively. PSO-SVM is developed as an efficient tool so that various SVMs can be used conveniently as the core of PSO-SVM for diagnosis of neuromuscular disorders. Copyright © 2013 Elsevier Ltd. All rights reserved.

Semi-supervised vibration-based classification and condition monitoring of compressors

NASA Astrophysics Data System (ADS)

Potočnik, Primož; Govekar, Edvard

2017-09-01

Semi-supervised vibration-based classification and condition monitoring of the reciprocating compressors installed in refrigeration appliances is proposed in this paper. The method addresses the problem of industrial condition monitoring where prior class definitions are often not available or difficult to obtain from local experts. The proposed method combines feature extraction, principal component analysis, and statistical analysis for the extraction of initial class representatives, and compares the capability of various classification methods, including discriminant analysis (DA), neural networks (NN), support vector machines (SVM), and extreme learning machines (ELM). The use of the method is demonstrated on a case study which was based on industrially acquired vibration measurements of reciprocating compressors during the production of refrigeration appliances. The paper presents a comparative qualitative analysis of the applied classifiers, confirming the good performance of several nonlinear classifiers. If the model parameters are properly selected, then very good classification performance can be obtained from NN trained by Bayesian regularization, SVM and ELM classifiers. The method can be effectively applied for the industrial condition monitoring of compressors.

Age group classification and gender detection based on forced expiratory spirometry.

PubMed

Cosgun, Sema; Ozbek, I Yucel

2015-08-01

This paper investigates the utility of forced expiratory spirometry (FES) test with efficient machine learning algorithms for the purpose of gender detection and age group classification. The proposed method has three main stages: feature extraction, training of the models and detection. In the first stage, some features are extracted from volume-time curve and expiratory flow-volume loop obtained from FES test. In the second stage, the probabilistic models for each gender and age group are constructed by training Gaussian mixture models (GMMs) and Support vector machine (SVM) algorithm. In the final stage, the gender (or age group) of test subject is estimated by using the trained GMM (or SVM) model. Experiments have been evaluated on a large database from 4571 subjects. The experimental results show that average correct classification rate performance of both GMM and SVM methods based on the FES test is more than 99.3 % and 96.8 % for gender and age group classification, respectively.

Parallel and Scalable Clustering and Classification for Big Data in Geosciences

NASA Astrophysics Data System (ADS)

Riedel, M.

2015-12-01

Machine learning, data mining, and statistical computing are common techniques to perform analysis in earth sciences. This contribution will focus on two concrete and widely used data analytics methods suitable to analyse 'big data' in the context of geoscience use cases: clustering and classification. From the broad class of available clustering methods we focus on the density-based spatial clustering of appliactions with noise (DBSCAN) algorithm that enables the identification of outliers or interesting anomalies. A new open source parallel and scalable DBSCAN implementation will be discussed in the light of a scientific use case that detects water mixing events in the Koljoefjords. The second technique we cover is classification, with a focus set on the support vector machines algorithm (SVMs), as one of the best out-of-the-box classification algorithm. A parallel and scalable SVM implementation will be discussed in the light of a scientific use case in the field of remote sensing with 52 different classes of land cover types.

Classification Algorithms for Big Data Analysis, a Map Reduce Approach

NASA Astrophysics Data System (ADS)

Ayma, V. A.; Ferreira, R. S.; Happ, P.; Oliveira, D.; Feitosa, R.; Costa, G.; Plaza, A.; Gamba, P.

2015-03-01

Since many years ago, the scientific community is concerned about how to increase the accuracy of different classification methods, and major achievements have been made so far. Besides this issue, the increasing amount of data that is being generated every day by remote sensors raises more challenges to be overcome. In this work, a tool within the scope of InterIMAGE Cloud Platform (ICP), which is an open-source, distributed framework for automatic image interpretation, is presented. The tool, named ICP: Data Mining Package, is able to perform supervised classification procedures on huge amounts of data, usually referred as big data, on a distributed infrastructure using Hadoop MapReduce. The tool has four classification algorithms implemented, taken from WEKA's machine learning library, namely: Decision Trees, Naïve Bayes, Random Forest and Support Vector Machines (SVM). The results of an experimental analysis using a SVM classifier on data sets of different sizes for different cluster configurations demonstrates the potential of the tool, as well as aspects that affect its performance.

A Machine Learning-based Method for Question Type Classification in Biomedical Question Answering.

PubMed

Sarrouti, Mourad; Ouatik El Alaoui, Said

2017-05-18

Biomedical question type classification is one of the important components of an automatic biomedical question answering system. The performance of the latter depends directly on the performance of its biomedical question type classification system, which consists of assigning a category to each question in order to determine the appropriate answer extraction algorithm. This study aims to automatically classify biomedical questions into one of the four categories: (1) yes/no, (2) factoid, (3) list, and (4) summary. In this paper, we propose a biomedical question type classification method based on machine learning approaches to automatically assign a category to a biomedical question. First, we extract features from biomedical questions using the proposed handcrafted lexico-syntactic patterns. Then, we feed these features for machine-learning algorithms. Finally, the class label is predicted using the trained classifiers. Experimental evaluations performed on large standard annotated datasets of biomedical questions, provided by the BioASQ challenge, demonstrated that our method exhibits significant improved performance when compared to four baseline systems. The proposed method achieves a roughly 10-point increase over the best baseline in terms of accuracy. Moreover, the obtained results show that using handcrafted lexico-syntactic patterns as features' provider of support vector machine (SVM) lead to the highest accuracy of 89.40 %. The proposed method can automatically classify BioASQ questions into one of the four categories: yes/no, factoid, list, and summary. Furthermore, the results demonstrated that our method produced the best classification performance compared to four baseline systems.

Active relearning for robust supervised classification of pulmonary emphysema

NASA Astrophysics Data System (ADS)

Raghunath, Sushravya; Rajagopalan, Srinivasan; Karwoski, Ronald A.; Bartholmai, Brian J.; Robb, Richard A.

2012-03-01

Radiologists are adept at recognizing the appearance of lung parenchymal abnormalities in CT scans. However, the inconsistent differential diagnosis, due to subjective aggregation, mandates supervised classification. Towards optimizing Emphysema classification, we introduce a physician-in-the-loop feedback approach in order to minimize uncertainty in the selected training samples. Using multi-view inductive learning with the training samples, an ensemble of Support Vector Machine (SVM) models, each based on a specific pair-wise dissimilarity metric, was constructed in less than six seconds. In the active relearning phase, the ensemble-expert label conflicts were resolved by an expert. This just-in-time feedback with unoptimized SVMs yielded 15% increase in classification accuracy and 25% reduction in the number of support vectors. The generality of relearning was assessed in the optimized parameter space of six different classifiers across seven dissimilarity metrics. The resultant average accuracy improved to 21%. The co-operative feedback method proposed here could enhance both diagnostic and staging throughput efficiency in chest radiology practice.

LBP and SIFT based facial expression recognition

NASA Astrophysics Data System (ADS)

Sumer, Omer; Gunes, Ece O.

2015-02-01

This study compares the performance of local binary patterns (LBP) and scale invariant feature transform (SIFT) with support vector machines (SVM) in automatic classification of discrete facial expressions. Facial expression recognition is a multiclass classification problem and seven classes; happiness, anger, sadness, disgust, surprise, fear and comtempt are classified. Using SIFT feature vectors and linear SVM, 93.1% mean accuracy is acquired on CK+ database. On the other hand, the performance of LBP-based classifier with linear SVM is reported on SFEW using strictly person independent (SPI) protocol. Seven-class mean accuracy on SFEW is 59.76%. Experiments on both databases showed that LBP features can be used in a fairly descriptive way if a good localization of facial points and partitioning strategy are followed.

Identifying Wrist Fracture Patients with High Accuracy by Automatic Categorization of X-ray Reports

PubMed Central

de Bruijn, Berry; Cranney, Ann; O’Donnell, Siobhan; Martin, Joel D.; Forster, Alan J.

2006-01-01

The authors performed this study to determine the accuracy of several text classification methods to categorize wrist x-ray reports. We randomly sampled 751 textual wrist x-ray reports. Two expert reviewers rated the presence (n = 301) or absence (n = 450) of an acute fracture of wrist. We developed two information retrieval (IR) text classification methods and a machine learning method using a support vector machine (TC-1). In cross-validation on the derivation set (n = 493), TC-1 outperformed the two IR based methods and six benchmark classifiers, including Naive Bayes and a Neural Network. In the validation set (n = 258), TC-1 demonstrated consistent performance with 93.8% accuracy; 95.5% sensitivity; 92.9% specificity; and 87.5% positive predictive value. TC-1 was easy to implement and superior in performance to the other classification methods. PMID:16929046

Emotional State Classification in Virtual Reality Using Wearable Electroencephalography

NASA Astrophysics Data System (ADS)

Suhaimi, N. S.; Teo, J.; Mountstephens, J.

2018-03-01

This paper presents the classification of emotions on EEG signals. One of the key issues in this research is the lack of mental classification using VR as the medium to stimulate emotion. The approach towards this research is by using K-nearest neighbor (KNN) and Support Vector Machine (SVM). Firstly, each of the participant will be required to wear the EEG headset and recording their brainwaves when they are immersed inside the VR. The data points are then marked if they showed any physical signs of emotion or by observing the brainwave pattern. Secondly, the data will then be tested and trained with KNN and SVM algorithms. The accuracy achieved from both methods were approximately 82% throughout the brainwave spectrum (α, β, γ, δ, θ). These methods showed promising results and will be further enhanced using other machine learning approaches in VR stimulus.

Application of support vector machine method for the analysis of absorption spectra of exhaled air of patients with broncho-pulmonary diseases

NASA Astrophysics Data System (ADS)

Bukreeva, Ekaterina B.; Bulanova, Anna A.; Kistenev, Yury V.; Kuzmin, Dmitry A.; Tuzikov, Sergei A.; Yumov, Evgeny L.

2014-11-01

The results of the joint use of laser photoacoustic spectroscopy and chemometrics methods in gas analysis of exhaled air of patients with respiratory diseases (chronic obstructive pulmonary disease, pneumonia and lung cancer) are presented. The absorption spectra of exhaled breath of all volunteers were measured, the classification methods of the scans of the absorption spectra were applied, the sensitivity/specificity of the classification results were determined. It were obtained a result of nosological in pairs classification for all investigated volunteers, indices of sensitivity and specificity.

Color Image Classification Using Block Matching and Learning

NASA Astrophysics Data System (ADS)

Kondo, Kazuki; Hotta, Seiji

In this paper, we propose block matching and learning for color image classification. In our method, training images are partitioned into small blocks. Given a test image, it is also partitioned into small blocks, and mean-blocks corresponding to each test block are calculated with neighbor training blocks. Our method classifies a test image into the class that has the shortest total sum of distances between mean blocks and test ones. We also propose a learning method for reducing memory requirement. Experimental results show that our classification outperforms other classifiers such as support vector machine with bag of keypoints.

Performance of a Machine Learning Classifier of Knee MRI Reports in Two Large Academic Radiology Practices: A Tool to Estimate Diagnostic Yield.

PubMed

Hassanpour, Saeed; Langlotz, Curtis P; Amrhein, Timothy J; Befera, Nicholas T; Lungren, Matthew P

2017-04-01

The purpose of this study is to evaluate the performance of a natural language processing (NLP) system in classifying a database of free-text knee MRI reports at two separate academic radiology practices. An NLP system that uses terms and patterns in manually classified narrative knee MRI reports was constructed. The NLP system was trained and tested on expert-classified knee MRI reports from two major health care organizations. Radiology reports were modeled in the training set as vectors, and a support vector machine framework was used to train the classifier. A separate test set from each organization was used to evaluate the performance of the system. We evaluated the performance of the system both within and across organizations. Standard evaluation metrics, such as accuracy, precision, recall, and F1 score (i.e., the weighted average of the precision and recall), and their respective 95% CIs were used to measure the efficacy of our classification system. The accuracy for radiology reports that belonged to the model's clinically significant concept classes after training data from the same institution was good, yielding an F1 score greater than 90% (95% CI, 84.6-97.3%). Performance of the classifier on cross-institutional application without institution-specific training data yielded F1 scores of 77.6% (95% CI, 69.5-85.7%) and 90.2% (95% CI, 84.5-95.9%) at the two organizations studied. The results show excellent accuracy by the NLP machine learning classifier in classifying free-text knee MRI reports, supporting the institution-independent reproducibility of knee MRI report classification. Furthermore, the machine learning classifier performed well on free-text knee MRI reports from another institution. These data support the feasibility of multiinstitutional classification of radiologic imaging text reports with a single machine learning classifier without requiring institution-specific training data.

Identification of Alzheimer's disease and mild cognitive impairment using multimodal sparse hierarchical extreme learning machine.

PubMed

Kim, Jongin; Lee, Boreom

2018-05-07

Different modalities such as structural MRI, FDG-PET, and CSF have complementary information, which is likely to be very useful for diagnosis of AD and MCI. Therefore, it is possible to develop a more effective and accurate AD/MCI automatic diagnosis method by integrating complementary information of different modalities. In this paper, we propose multi-modal sparse hierarchical extreme leaning machine (MSH-ELM). We used volume and mean intensity extracted from 93 regions of interest (ROIs) as features of MRI and FDG-PET, respectively, and used p-tau, t-tau, and Aβ42 as CSF features. In detail, high-level representation was individually extracted from each of MRI, FDG-PET, and CSF using a stacked sparse extreme learning machine auto-encoder (sELM-AE). Then, another stacked sELM-AE was devised to acquire a joint hierarchical feature representation by fusing the high-level representations obtained from each modality. Finally, we classified joint hierarchical feature representation using a kernel-based extreme learning machine (KELM). The results of MSH-ELM were compared with those of conventional ELM, single kernel support vector machine (SK-SVM), multiple kernel support vector machine (MK-SVM) and stacked auto-encoder (SAE). Performance was evaluated through 10-fold cross-validation. In the classification of AD vs. HC and MCI vs. HC problem, the proposed MSH-ELM method showed mean balanced accuracies of 96.10% and 86.46%, respectively, which is much better than those of competing methods. In summary, the proposed algorithm exhibits consistently better performance than SK-SVM, ELM, MK-SVM and SAE in the two binary classification problems (AD vs. HC and MCI vs. HC). © 2018 Wiley Periodicals, Inc.

Near infrared spectroscopy is suitable for the classification of hazelnuts according to Protected Designation of Origin.

PubMed

Moscetti, Roberto; Radicetti, Emanuele; Monarca, Danilo; Cecchini, Massimo; Massantini, Riccardo

2015-10-01

This study investigates the possibility of using near infrared spectroscopy for the authentication of the 'Nocciola Romana' hazelnut (Corylus avellana L. cvs Tonda Gentile Romana and Nocchione) as a Protected Designation of Origin (PDO) hazelnut from central Italy. Algorithms for the selection of the optimal pretreatments were tested in combination with the following discriminant routines: k-nearest neighbour, soft independent modelling of class analogy, partial least squares discriminant analysis and support vector machine discriminant analysis. The best results were obtained using a support vector machine discriminant analysis routine. Thus, classification performance rates with specificities, sensitivities and accuracies as high as 96.0%, 95.0% and 95.5%, respectively, were achieved. Various pretreatments, such as standard normal variate, mean centring and a Savitzky-Golay filter with seven smoothing points, were used. The optimal wavelengths for classification were mainly correlated with lipids, although some contribution from minor constituents, such as proteins and carbohydrates, was also observed. Near infrared spectroscopy could classify hazelnut according to the PDO 'Nocciola Romana' designation. Thus, the experimentation lays the foundations for a rapid, online, authentication system for hazelnut. However, model robustness should be improved taking into account agro-pedo-climatic growing conditions. © 2014 Society of Chemical Industry.

Voltammetric Electronic Tongue and Support Vector Machines for Identification of Selected Features in Mexican Coffee

PubMed Central

Domínguez, Rocio Berenice; Moreno-Barón, Laura; Muñoz, Roberto; Gutiérrez, Juan Manuel

2014-01-01

This paper describes a new method based on a voltammetric electronic tongue (ET) for the recognition of distinctive features in coffee samples. An ET was directly applied to different samples from the main Mexican coffee regions without any pretreatment before the analysis. The resulting electrochemical information was modeled with two different mathematical tools, namely Linear Discriminant Analysis (LDA) and Support Vector Machines (SVM). Growing conditions (i.e., organic or non-organic practices and altitude of crops) were considered for a first classification. LDA results showed an average discrimination rate of 88% ± 6.53% while SVM successfully accomplished an overall accuracy of 96.4% ± 3.50% for the same task. A second classification based on geographical origin of samples was carried out. Results showed an overall accuracy of 87.5% ± 7.79% for LDA and a superior performance of 97.5% ± 3.22% for SVM. Given the complexity of coffee samples, the high accuracy percentages achieved by ET coupled with SVM in both classification problems suggested a potential applicability of ET in the assessment of selected coffee features with a simpler and faster methodology along with a null sample pretreatment. In addition, the proposed method can be applied to authentication assessment while improving cost, time and accuracy of the general procedure. PMID:25254303

Voltammetric electronic tongue and support vector machines for identification of selected features in Mexican coffee.

PubMed

Domínguez, Rocio Berenice; Moreno-Barón, Laura; Muñoz, Roberto; Gutiérrez, Juan Manuel

2014-09-24

This paper describes a new method based on a voltammetric electronic tongue (ET) for the recognition of distinctive features in coffee samples. An ET was directly applied to different samples from the main Mexican coffee regions without any pretreatment before the analysis. The resulting electrochemical information was modeled with two different mathematical tools, namely Linear Discriminant Analysis (LDA) and Support Vector Machines (SVM). Growing conditions (i.e., organic or non-organic practices and altitude of crops) were considered for a first classification. LDA results showed an average discrimination rate of 88% ± 6.53% while SVM successfully accomplished an overall accuracy of 96.4% ± 3.50% for the same task. A second classification based on geographical origin of samples was carried out. Results showed an overall accuracy of 87.5% ± 7.79% for LDA and a superior performance of 97.5% ± 3.22% for SVM. Given the complexity of coffee samples, the high accuracy percentages achieved by ET coupled with SVM in both classification problems suggested a potential applicability of ET in the assessment of selected coffee features with a simpler and faster methodology along with a null sample pretreatment. In addition, the proposed method can be applied to authentication assessment while improving cost, time and accuracy of the general procedure.

Age dependent electroencephalographic changes in attention-deficit/hyperactivity disorder (ADHD).

PubMed

Poil, S-S; Bollmann, S; Ghisleni, C; O'Gorman, R L; Klaver, P; Ball, J; Eich-Höchli, D; Brandeis, D; Michels, L

2014-08-01

Objective biomarkers for attention-deficit/hyperactivity disorder (ADHD) could improve diagnostics or treatment monitoring of this psychiatric disorder. The resting electroencephalogram (EEG) provides non-invasive spectral markers of brain function and development. Their accuracy as ADHD markers is increasingly questioned but may improve with pattern classification. This study provides an integrated analysis of ADHD and developmental effects in children and adults using regression analysis and support vector machine classification of spectral resting (eyes-closed) EEG biomarkers in order to clarify their diagnostic value. ADHD effects on EEG strongly depend on age and frequency. We observed typical non-linear developmental decreases in delta and theta power for both ADHD and control groups. However, for ADHD adults we found a slowing in alpha frequency combined with a higher power in alpha-1 (8-10Hz) and beta (13-30Hz). Support vector machine classification of ADHD adults versus controls yielded a notable cross validated sensitivity of 67% and specificity of 83% using power and central frequency from all frequency bands. ADHD children were not classified convincingly with these markers. Resting state electrophysiology is altered in ADHD, and these electrophysiological impairments persist into adulthood. Spectral biomarkers may have both diagnostic and prognostic value. Copyright © 2014 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved.

Computer-aided classification of Alzheimer's disease based on support vector machine with combination of cerebral image features in MRI

NASA Astrophysics Data System (ADS)

Jongkreangkrai, C.; Vichianin, Y.; Tocharoenchai, C.; Arimura, H.; Alzheimer's Disease Neuroimaging Initiative

2016-03-01

Several studies have differentiated Alzheimer's disease (AD) using cerebral image features derived from MR brain images. In this study, we were interested in combining hippocampus and amygdala volumes and entorhinal cortex thickness to improve the performance of AD differentiation. Thus, our objective was to investigate the useful features obtained from MRI for classification of AD patients using support vector machine (SVM). T1-weighted MR brain images of 100 AD patients and 100 normal subjects were processed using FreeSurfer software to measure hippocampus and amygdala volumes and entorhinal cortex thicknesses in both brain hemispheres. Relative volumes of hippocampus and amygdala were calculated to correct variation in individual head size. SVM was employed with five combinations of features (H: hippocampus relative volumes, A: amygdala relative volumes, E: entorhinal cortex thicknesses, HA: hippocampus and amygdala relative volumes and ALL: all features). Receiver operating characteristic (ROC) analysis was used to evaluate the method. AUC values of five combinations were 0.8575 (H), 0.8374 (A), 0.8422 (E), 0.8631 (HA) and 0.8906 (ALL). Although “ALL” provided the highest AUC, there were no statistically significant differences among them except for “A” feature. Our results showed that all suggested features may be feasible for computer-aided classification of AD patients.

A Fault Alarm and Diagnosis Method Based on Sensitive Parameters and Support Vector Machine

NASA Astrophysics Data System (ADS)

Zhang, Jinjie; Yao, Ziyun; Lv, Zhiquan; Zhu, Qunxiong; Xu, Fengtian; Jiang, Zhinong

2015-08-01

Study on the extraction of fault feature and the diagnostic technique of reciprocating compressor is one of the hot research topics in the field of reciprocating machinery fault diagnosis at present. A large number of feature extraction and classification methods have been widely applied in the related research, but the practical fault alarm and the accuracy of diagnosis have not been effectively improved. Developing feature extraction and classification methods to meet the requirements of typical fault alarm and automatic diagnosis in practical engineering is urgent task. The typical mechanical faults of reciprocating compressor are presented in the paper, and the existing data of online monitoring system is used to extract fault feature parameters within 15 types in total; the inner sensitive connection between faults and the feature parameters has been made clear by using the distance evaluation technique, also sensitive characteristic parameters of different faults have been obtained. On this basis, a method based on fault feature parameters and support vector machine (SVM) is developed, which will be applied to practical fault diagnosis. A better ability of early fault warning has been proved by the experiment and the practical fault cases. Automatic classification by using the SVM to the data of fault alarm has obtained better diagnostic accuracy.

Research on the Relationship between Reaction Ability and Mental State for Online Assessment of Driving Fatigue.

PubMed

Guo, Mengzhu; Li, Shiwu; Wang, Linhong; Chai, Meng; Chen, Facheng; Wei, Yunong

2016-11-24

Background: Driving fatigue affects the reaction ability of a driver. The aim of this research is to analyze the relationship between driving fatigue, physiological signals and driver's reaction time. Methods: Twenty subjects were tested during driving. Data pertaining to reaction time and physiological signals including electroencephalograph (EEG) were collected from twenty simulation experiments. Grey correlation analysis was used to select the input variable of the classification model. A support vector machine was used to divide the mental state into three levels. The penalty factor for the model was optimized using a genetic algorithm. Results: The results show that α/β has the greatest correlation to reaction time. The classification results show an accuracy of 86%, a sensitivity of 87.5% and a specificity of 85.53%. The average increase of reaction time is 16.72% from alert state to fatigued state. Females have a faster decrease in reaction ability than males as driving fatigue accumulates. Elderly drivers have longer reaction times than the young. Conclusions: A grey correlation analysis can be used to improve the classification accuracy of the support vector machine (SVM) model. This paper provides basic research that online detection of fatigue can be performed using only a simple device, which is more comfortable for users.

Research on the Relationship between Reaction Ability and Mental State for Online Assessment of Driving Fatigue

PubMed Central

Guo, Mengzhu; Li, Shiwu; Wang, Linhong; Chai, Meng; Chen, Facheng; Wei, Yunong

2016-01-01

Background: Driving fatigue affects the reaction ability of a driver. The aim of this research is to analyze the relationship between driving fatigue, physiological signals and driver’s reaction time. Methods: Twenty subjects were tested during driving. Data pertaining to reaction time and physiological signals including electroencephalograph (EEG) were collected from twenty simulation experiments. Grey correlation analysis was used to select the input variable of the classification model. A support vector machine was used to divide the mental state into three levels. The penalty factor for the model was optimized using a genetic algorithm. Results: The results show that α/β has the greatest correlation to reaction time. The classification results show an accuracy of 86%, a sensitivity of 87.5% and a specificity of 85.53%. The average increase of reaction time is 16.72% from alert state to fatigued state. Females have a faster decrease in reaction ability than males as driving fatigue accumulates. Elderly drivers have longer reaction times than the young. Conclusions: A grey correlation analysis can be used to improve the classification accuracy of the support vector machine (SVM) model. This paper provides basic research that online detection of fatigue can be performed using only a simple device, which is more comfortable for users. PMID:27886139

Morphological and wavelet features towards sonographic thyroid nodules evaluation.

PubMed

Tsantis, Stavros; Dimitropoulos, Nikos; Cavouras, Dionisis; Nikiforidis, George

2009-03-01

This paper presents a computer-based classification scheme that utilized various morphological and novel wavelet-based features towards malignancy risk evaluation of thyroid nodules in ultrasonography. The study comprised 85 ultrasound images-patients that were cytological confirmed (54 low-risk and 31 high-risk). A set of 20 features (12 based on nodules boundary shape and 8 based on wavelet local maxima located within each nodule) has been generated. Two powerful pattern recognition algorithms (support vector machines and probabilistic neural networks) have been designed and developed in order to quantify the power of differentiation of the introduced features. A comparative study has also been held, in order to estimate the impact speckle had onto the classification procedure. The diagnostic sensitivity and specificity of both classifiers was made by means of receiver operating characteristics (ROC) analysis. In the speckle-free feature set, the area under the ROC curve was 0.96 for the support vector machines classifier whereas for the probabilistic neural networks was 0.91. In the feature set with speckle, the corresponding areas under the ROC curves were 0.88 and 0.86 respectively for the two classifiers. The proposed features can increase the classification accuracy and decrease the rate of missing and misdiagnosis in thyroid cancer control.

The generalization ability of online SVM classification based on Markov sampling.

PubMed

Xu, Jie; Yan Tang, Yuan; Zou, Bin; Xu, Zongben; Li, Luoqing; Lu, Yang

2015-03-01

In this paper, we consider online support vector machine (SVM) classification learning algorithms with uniformly ergodic Markov chain (u.e.M.c.) samples. We establish the bound on the misclassification error of an online SVM classification algorithm with u.e.M.c. samples based on reproducing kernel Hilbert spaces and obtain a satisfactory convergence rate. We also introduce a novel online SVM classification algorithm based on Markov sampling, and present the numerical studies on the learning ability of online SVM classification based on Markov sampling for benchmark repository. The numerical studies show that the learning performance of the online SVM classification algorithm based on Markov sampling is better than that of classical online SVM classification based on random sampling as the size of training samples is larger.

Analysis and comparison of sleeping posture classification methods using pressure sensitive bed system.

PubMed

Hsia, C C; Liou, K J; Aung, A P W; Foo, V; Huang, W; Biswas, J

2009-01-01

Pressure ulcers are common problems for bedridden patients. Caregivers need to reposition the sleeping posture of a patient every two hours in order to reduce the risk of getting ulcers. This study presents the use of Kurtosis and skewness estimation, principal component analysis (PCA) and support vector machines (SVMs) for sleeping posture classification using cost-effective pressure sensitive mattress that can help caregivers to make correct sleeping posture changes for the prevention of pressure ulcers.

Using Support Vector Machine Ensembles for Target Audience Classification on Twitter

PubMed Central

Lo, Siaw Ling; Chiong, Raymond; Cornforth, David

2015-01-01

The vast amount and diversity of the content shared on social media can pose a challenge for any business wanting to use it to identify potential customers. In this paper, our aim is to investigate the use of both unsupervised and supervised learning methods for target audience classification on Twitter with minimal annotation efforts. Topic domains were automatically discovered from contents shared by followers of an account owner using Twitter Latent Dirichlet Allocation (LDA). A Support Vector Machine (SVM) ensemble was then trained using contents from different account owners of the various topic domains identified by Twitter LDA. Experimental results show that the methods presented are able to successfully identify a target audience with high accuracy. In addition, we show that using a statistical inference approach such as bootstrapping in over-sampling, instead of using random sampling, to construct training datasets can achieve a better classifier in an SVM ensemble. We conclude that such an ensemble system can take advantage of data diversity, which enables real-world applications for differentiating prospective customers from the general audience, leading to business advantage in the crowded social media space. PMID:25874768

Using support vector machine ensembles for target audience classification on Twitter.

PubMed

Lo, Siaw Ling; Chiong, Raymond; Cornforth, David

2015-01-01

The vast amount and diversity of the content shared on social media can pose a challenge for any business wanting to use it to identify potential customers. In this paper, our aim is to investigate the use of both unsupervised and supervised learning methods for target audience classification on Twitter with minimal annotation efforts. Topic domains were automatically discovered from contents shared by followers of an account owner using Twitter Latent Dirichlet Allocation (LDA). A Support Vector Machine (SVM) ensemble was then trained using contents from different account owners of the various topic domains identified by Twitter LDA. Experimental results show that the methods presented are able to successfully identify a target audience with high accuracy. In addition, we show that using a statistical inference approach such as bootstrapping in over-sampling, instead of using random sampling, to construct training datasets can achieve a better classifier in an SVM ensemble. We conclude that such an ensemble system can take advantage of data diversity, which enables real-world applications for differentiating prospective customers from the general audience, leading to business advantage in the crowded social media space.

Study of support vector machine and serum surface-enhanced Raman spectroscopy for noninvasive esophageal cancer detection

NASA Astrophysics Data System (ADS)

Li, Shao-Xin; Zeng, Qiu-Yao; Li, Lin-Fang; Zhang, Yan-Jiao; Wan, Ming-Ming; Liu, Zhi-Ming; Xiong, Hong-Lian; Guo, Zhou-Yi; Liu, Song-Hao

2013-02-01

The ability of combining serum surface-enhanced Raman spectroscopy (SERS) with support vector machine (SVM) for improving classification esophageal cancer patients from normal volunteers is investigated. Two groups of serum SERS spectra based on silver nanoparticles (AgNPs) are obtained: one group from patients with pathologically confirmed esophageal cancer (n=30) and the other group from healthy volunteers (n=31). Principal components analysis (PCA), conventional SVM (C-SVM) and conventional SVM combination with PCA (PCA-SVM) methods are implemented to classify the same spectral dataset. Results show that a diagnostic accuracy of 77.0% is acquired for PCA technique, while diagnostic accuracies of 83.6% and 85.2% are obtained for C-SVM and PCA-SVM methods based on radial basis functions (RBF) models. The results prove that RBF SVM models are superior to PCA algorithm in classification serum SERS spectra. The study demonstrates that serum SERS in combination with SVM technique has great potential to provide an effective and accurate diagnostic schema for noninvasive detection of esophageal cancer.

Exploitation of multi-temporal Earth Observation imagery for monitoring land cover change in mining sites

NASA Astrophysics Data System (ADS)

Petropoulos, G.; Partsinevelos, P.; Mitraka, Z.

2012-04-01

Surface mining has been shown to cause intensive environmental degradation in terms of landscape, vegetation and biological communities. Nowadays, the commercial availability of remote sensing imagery at high spatiotemporal scales, has improved dramatically our ability to monitor surface mining activity and evaluate its impact on the environment and society. In this study we investigate the potential use of Landsat TM imagery combined with diverse classification techniques, namely artificial neural networks and support vector machines for delineating mining exploration and assessing its effect on vegetation in various surface mining sites in the Greek island of Milos. Assessment of the mining impact in the study area is validated through the analysis of available QuickBird imagery acquired nearly concurrently to the TM overpasses. Results indicate the capability of the TM sensor combined with the image analysis applied herein as a potential economically viable solution to provide rapidly and at regular time intervals information on mining activity and its impact to the local environment. KEYWORDS: mining environmental impact, remote sensing, image classification, change detection, land reclamation, support vector machines, neural networks

Improving medical diagnosis reliability using Boosted C5.0 decision tree empowered by Particle Swarm Optimization.

PubMed

Pashaei, Elnaz; Ozen, Mustafa; Aydin, Nizamettin

2015-08-01

Improving accuracy of supervised classification algorithms in biomedical applications is one of active area of research. In this study, we improve the performance of Particle Swarm Optimization (PSO) combined with C4.5 decision tree (PSO+C4.5) classifier by applying Boosted C5.0 decision tree as the fitness function. To evaluate the effectiveness of our proposed method, it is implemented on 1 microarray dataset and 5 different medical data sets obtained from UCI machine learning databases. Moreover, the results of PSO + Boosted C5.0 implementation are compared to eight well-known benchmark classification methods (PSO+C4.5, support vector machine under the kernel of Radial Basis Function, Classification And Regression Tree (CART), C4.5 decision tree, C5.0 decision tree, Boosted C5.0 decision tree, Naive Bayes and Weighted K-Nearest neighbor). Repeated five-fold cross-validation method was used to justify the performance of classifiers. Experimental results show that our proposed method not only improve the performance of PSO+C4.5 but also obtains higher classification accuracy compared to the other classification methods.

Support Vector Machine Model for Automatic Detection and Classification of Seismic Events

NASA Astrophysics Data System (ADS)

Barros, Vesna; Barros, Lucas

2016-04-01

The automated processing of multiple seismic signals to detect, localize and classify seismic events is a central tool in both natural hazards monitoring and nuclear treaty verification. However, false detections and missed detections caused by station noise and incorrect classification of arrivals are still an issue and the events are often unclassified or poorly classified. Thus, machine learning techniques can be used in automatic processing for classifying the huge database of seismic recordings and provide more confidence in the final output. Applied in the context of the International Monitoring System (IMS) - a global sensor network developed for the Comprehensive Nuclear-Test-Ban Treaty (CTBT) - we propose a fully automatic method for seismic event detection and classification based on a supervised pattern recognition technique called the Support Vector Machine (SVM). According to Kortström et al., 2015, the advantages of using SVM are handleability of large number of features and effectiveness in high dimensional spaces. Our objective is to detect seismic events from one IMS seismic station located in an area of high seismicity and mining activity and classify them as earthquakes or quarry blasts. It is expected to create a flexible and easily adjustable SVM method that can be applied in different regions and datasets. Taken a step further, accurate results for seismic stations could lead to a modification of the model and its parameters to make it applicable to other waveform technologies used to monitor nuclear explosions such as infrasound and hydroacoustic waveforms. As an authorized user, we have direct access to all IMS data and bulletins through a secure signatory account. A set of significant seismic waveforms containing different types of events (e.g. earthquake, quarry blasts) and noise is being analysed to train the model and learn the typical pattern of the signal from these events. Moreover, comparing the performance of the support-vector network to various classical learning algorithms used before in seismic detection and classification is an essential final step to analyze the advantages and disadvantages of the model.

An Automated and Intelligent Medical Decision Support System for Brain MRI Scans Classification.

PubMed

Siddiqui, Muhammad Faisal; Reza, Ahmed Wasif; Kanesan, Jeevan

2015-01-01

A wide interest has been observed in the medical health care applications that interpret neuroimaging scans by machine learning systems. This research proposes an intelligent, automatic, accurate, and robust classification technique to classify the human brain magnetic resonance image (MRI) as normal or abnormal, to cater down the human error during identifying the diseases in brain MRIs. In this study, fast discrete wavelet transform (DWT), principal component analysis (PCA), and least squares support vector machine (LS-SVM) are used as basic components. Firstly, fast DWT is employed to extract the salient features of brain MRI, followed by PCA, which reduces the dimensions of the features. These reduced feature vectors also shrink the memory storage consumption by 99.5%. At last, an advanced classification technique based on LS-SVM is applied to brain MR image classification using reduced features. For improving the efficiency, LS-SVM is used with non-linear radial basis function (RBF) kernel. The proposed algorithm intelligently determines the optimized values of the hyper-parameters of the RBF kernel and also applied k-fold stratified cross validation to enhance the generalization of the system. The method was tested by 340 patients' benchmark datasets of T1-weighted and T2-weighted scans. From the analysis of experimental results and performance comparisons, it is observed that the proposed medical decision support system outperformed all other modern classifiers and achieves 100% accuracy rate (specificity/sensitivity 100%/100%). Furthermore, in terms of computation time, the proposed technique is significantly faster than the recent well-known methods, and it improves the efficiency by 71%, 3%, and 4% on feature extraction stage, feature reduction stage, and classification stage, respectively. These results indicate that the proposed well-trained machine learning system has the potential to make accurate predictions about brain abnormalities from the individual subjects, therefore, it can be used as a significant tool in clinical practice.

An ensemble approach to protein fold classification by integration of template-based assignment and support vector machine classifier.

PubMed

Xia, Jiaqi; Peng, Zhenling; Qi, Dawei; Mu, Hongbo; Yang, Jianyi

2017-03-15

Protein fold classification is a critical step in protein structure prediction. There are two possible ways to classify protein folds. One is through template-based fold assignment and the other is ab-initio prediction using machine learning algorithms. Combination of both solutions to improve the prediction accuracy was never explored before. We developed two algorithms, HH-fold and SVM-fold for protein fold classification. HH-fold is a template-based fold assignment algorithm using the HHsearch program. SVM-fold is a support vector machine-based ab-initio classification algorithm, in which a comprehensive set of features are extracted from three complementary sequence profiles. These two algorithms are then combined, resulting to the ensemble approach TA-fold. We performed a comprehensive assessment for the proposed methods by comparing with ab-initio methods and template-based threading methods on six benchmark datasets. An accuracy of 0.799 was achieved by TA-fold on the DD dataset that consists of proteins from 27 folds. This represents improvement of 5.4-11.7% over ab-initio methods. After updating this dataset to include more proteins in the same folds, the accuracy increased to 0.971. In addition, TA-fold achieved >0.9 accuracy on a large dataset consisting of 6451 proteins from 184 folds. Experiments on the LE dataset show that TA-fold consistently outperforms other threading methods at the family, superfamily and fold levels. The success of TA-fold is attributed to the combination of template-based fold assignment and ab-initio classification using features from complementary sequence profiles that contain rich evolution information. http://yanglab.nankai.edu.cn/TA-fold/. yangjy@nankai.edu.cn or mhb-506@163.com. Supplementary data are available at Bioinformatics online. © The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com

Prediction of Human Intestinal Absorption of Compounds Using Artificial Intelligence Techniques.

PubMed

Kumar, Rajnish; Sharma, Anju; Siddiqui, Mohammed Haris; Tiwari, Rajesh Kumar

2017-01-01

Information about Pharmacokinetics of compounds is an essential component of drug design and development. Modeling the pharmacokinetic properties require identification of the factors effecting absorption, distribution, metabolism and excretion of compounds. There have been continuous attempts in the prediction of intestinal absorption of compounds using various Artificial intelligence methods in the effort to reduce the attrition rate of drug candidates entering to preclinical and clinical trials. Currently, there are large numbers of individual predictive models available for absorption using machine learning approaches. Six Artificial intelligence methods namely, Support vector machine, k- nearest neighbor, Probabilistic neural network, Artificial neural network, Partial least square and Linear discriminant analysis were used for prediction of absorption of compounds. Prediction accuracy of Support vector machine, k- nearest neighbor, Probabilistic neural network, Artificial neural network, Partial least square and Linear discriminant analysis for prediction of intestinal absorption of compounds was found to be 91.54%, 88.33%, 84.30%, 86.51%, 79.07% and 80.08% respectively. Comparative analysis of all the six prediction models suggested that Support vector machine with Radial basis function based kernel is comparatively better for binary classification of compounds using human intestinal absorption and may be useful at preliminary stages of drug design and development. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

Wavelet Entropy and Directed Acyclic Graph Support Vector Machine for Detection of Patients with Unilateral Hearing Loss in MRI Scanning

PubMed Central

Wang, Shuihua; Yang, Ming; Du, Sidan; Yang, Jiquan; Liu, Bin; Gorriz, Juan M.; Ramírez, Javier; Yuan, Ti-Fei; Zhang, Yudong

2016-01-01

Highlights We develop computer-aided diagnosis system for unilateral hearing loss detection in structural magnetic resonance imaging.Wavelet entropy is introduced to extract image global features from brain images. Directed acyclic graph is employed to endow support vector machine an ability to handle multi-class problems.The developed computer-aided diagnosis system achieves an overall accuracy of 95.1% for this three-class problem of differentiating left-sided and right-sided hearing loss from healthy controls. Aim: Sensorineural hearing loss (SNHL) is correlated to many neurodegenerative disease. Now more and more computer vision based methods are using to detect it in an automatic way. Materials: We have in total 49 subjects, scanned by 3.0T MRI (Siemens Medical Solutions, Erlangen, Germany). The subjects contain 14 patients with right-sided hearing loss (RHL), 15 patients with left-sided hearing loss (LHL), and 20 healthy controls (HC). Method: We treat this as a three-class classification problem: RHL, LHL, and HC. Wavelet entropy (WE) was selected from the magnetic resonance images of each subjects, and then submitted to a directed acyclic graph support vector machine (DAG-SVM). Results: The 10 repetition results of 10-fold cross validation shows 3-level decomposition will yield an overall accuracy of 95.10% for this three-class classification problem, higher than feedforward neural network, decision tree, and naive Bayesian classifier. Conclusions: This computer-aided diagnosis system is promising. We hope this study can attract more computer vision method for detecting hearing loss. PMID:27807415

Wavelet Entropy and Directed Acyclic Graph Support Vector Machine for Detection of Patients with Unilateral Hearing Loss in MRI Scanning.

PubMed

Wang, Shuihua; Yang, Ming; Du, Sidan; Yang, Jiquan; Liu, Bin; Gorriz, Juan M; Ramírez, Javier; Yuan, Ti-Fei; Zhang, Yudong

2016-01-01

Highlights We develop computer-aided diagnosis system for unilateral hearing loss detection in structural magnetic resonance imaging.Wavelet entropy is introduced to extract image global features from brain images. Directed acyclic graph is employed to endow support vector machine an ability to handle multi-class problems.The developed computer-aided diagnosis system achieves an overall accuracy of 95.1% for this three-class problem of differentiating left-sided and right-sided hearing loss from healthy controls. Aim: Sensorineural hearing loss (SNHL) is correlated to many neurodegenerative disease. Now more and more computer vision based methods are using to detect it in an automatic way. Materials: We have in total 49 subjects, scanned by 3.0T MRI (Siemens Medical Solutions, Erlangen, Germany). The subjects contain 14 patients with right-sided hearing loss (RHL), 15 patients with left-sided hearing loss (LHL), and 20 healthy controls (HC). Method: We treat this as a three-class classification problem: RHL, LHL, and HC. Wavelet entropy (WE) was selected from the magnetic resonance images of each subjects, and then submitted to a directed acyclic graph support vector machine (DAG-SVM). Results: The 10 repetition results of 10-fold cross validation shows 3-level decomposition will yield an overall accuracy of 95.10% for this three-class classification problem, higher than feedforward neural network, decision tree, and naive Bayesian classifier. Conclusions: This computer-aided diagnosis system is promising. We hope this study can attract more computer vision method for detecting hearing loss.

A Bag of Concepts Approach for Biomedical Document Classification Using Wikipedia Knowledge.

PubMed

Mouriño-García, Marcos A; Pérez-Rodríguez, Roberto; Anido-Rifón, Luis E

2017-01-01

The ability to efficiently review the existing literature is essential for the rapid progress of research. This paper describes a classifier of text documents, represented as vectors in spaces of Wikipedia concepts, and analyses its suitability for classification of Spanish biomedical documents when only English documents are available for training. We propose the cross-language concept matching (CLCM) technique, which relies on Wikipedia interlanguage links to convert concept vectors from the Spanish to the English space. The performance of the classifier is compared to several baselines: a classifier based on machine translation, a classifier that represents documents after performing Explicit Semantic Analysis (ESA), and a classifier that uses a domain-specific semantic an- notator (MetaMap). The corpus used for the experiments (Cross-Language UVigoMED) was purpose-built for this study, and it is composed of 12,832 English and 2,184 Spanish MEDLINE abstracts. The performance of our approach is superior to any other state-of-the art classifier in the benchmark, with performance increases up to: 124% over classical machine translation, 332% over MetaMap, and 60 times over the classifier based on ESA. The results have statistical significance, showing p-values < 0.0001. Using knowledge mined from Wikipedia to represent documents as vectors in a space of Wikipedia concepts and translating vectors between language-specific concept spaces, a cross-language classifier can be built, and it performs better than several state-of-the-art classifiers. Schattauer GmbH.

A Bag of Concepts Approach for Biomedical Document Classification Using Wikipedia Knowledge*. Spanish-English Cross-language Case Study.

PubMed

Mouriño-García, Marcos A; Pérez-Rodríguez, Roberto; Anido-Rifón, Luis E

2017-10-26

The ability to efficiently review the existing literature is essential for the rapid progress of research. This paper describes a classifier of text documents, represented as vectors in spaces of Wikipedia concepts, and analyses its suitability for classification of Spanish biomedical documents when only English documents are available for training. We propose the cross-language concept matching (CLCM) technique, which relies on Wikipedia interlanguage links to convert concept vectors from the Spanish to the English space. The performance of the classifier is compared to several baselines: a classifier based on machine translation, a classifier that represents documents after performing Explicit Semantic Analysis (ESA), and a classifier that uses a domain-specific semantic annotator (MetaMap). The corpus used for the experiments (Cross-Language UVigoMED) was purpose-built for this study, and it is composed of 12,832 English and 2,184 Spanish MEDLINE abstracts. The performance of our approach is superior to any other state-of-the art classifier in the benchmark, with performance increases up to: 124% over classical machine translation, 332% over MetaMap, and 60 times over the classifier based on ESA. The results have statistical significance, showing p-values < 0.0001. Using knowledge mined from Wikipedia to represent documents as vectors in a space of Wikipedia concepts and translating vectors between language-specific concept spaces, a cross-language classifier can be built, and it performs better than several state-of-the-art classifiers.

Multiscale asymmetric orthogonal wavelet kernel for linear programming support vector learning and nonlinear dynamic systems identification.

PubMed

Lu, Zhao; Sun, Jing; Butts, Kenneth

2014-05-01

Support vector regression for approximating nonlinear dynamic systems is more delicate than the approximation of indicator functions in support vector classification, particularly for systems that involve multitudes of time scales in their sampled data. The kernel used for support vector learning determines the class of functions from which a support vector machine can draw its solution, and the choice of kernel significantly influences the performance of a support vector machine. In this paper, to bridge the gap between wavelet multiresolution analysis and kernel learning, the closed-form orthogonal wavelet is exploited to construct new multiscale asymmetric orthogonal wavelet kernels for linear programming support vector learning. The closed-form multiscale orthogonal wavelet kernel provides a systematic framework to implement multiscale kernel learning via dyadic dilations and also enables us to represent complex nonlinear dynamics effectively. To demonstrate the superiority of the proposed multiscale wavelet kernel in identifying complex nonlinear dynamic systems, two case studies are presented that aim at building parallel models on benchmark datasets. The development of parallel models that address the long-term/mid-term prediction issue is more intricate and challenging than the identification of series-parallel models where only one-step ahead prediction is required. Simulation results illustrate the effectiveness of the proposed multiscale kernel learning.

An automatic method for skeletal patterns classification using craniomaxillary variables on a Colombian population.

PubMed

Niño-Sandoval, Tania Camila; Guevara Perez, Sonia V; González, Fabio A; Jaque, Robinson Andrés; Infante-Contreras, Clementina

2016-04-01

The mandibular bone is an important part of the forensic facial reconstruction and it has the possibility of getting lost in skeletonized remains; for this reason, it is necessary to facilitate the identification process simulating the mandibular position only through craniomaxillary measures, for this task, different modeling techniques have been performed, but they only contemplate a straight facial profile that belong to skeletal pattern Class I, but the 24.5% corresponding to the Colombian skeletal patterns Class II and III are not taking into account, besides, craniofacial measures do not follow a parametric trend or a normal distribution. The aim of this study was to employ an automatic non-parametric method as the Support Vector Machines to classify skeletal patterns through craniomaxillary variables, in order to simulate the natural mandibular position on a contemporary Colombian sample. Lateral cephalograms (229) of Colombian young adults of both sexes were collected. Landmark coordinates protocols were used to create craniomaxillary variables. A Support Vector Machine with a linear kernel classifier model was trained on a subset of the available data and evaluated over the remaining samples. The weights of the model were used to select the 10 best variables for classification accuracy. An accuracy of 74.51% was obtained, defined by Pr-A-N, N-Pr-A, A-N-Pr, A-Te-Pr, A-Pr-Rhi, Rhi-A-Pr, Pr-A-Te, Te-Pr-A, Zm-A-Pr and PNS-A-Pr angles. The Class Precision and the Class Recall showed a correct distinction of the Class II from the Class III and vice versa. Support Vector Machines created an important model of classification of skeletal patterns using craniomaxillary variables that are not commonly used in the literature and could be applicable to the 24.5% of the contemporary Colombian sample. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Support Vector Machines for Multitemporal and Multisensor Change Detection in a Mining Area

NASA Astrophysics Data System (ADS)

Hecheltjen, Antje; Waske, Bjorn; Thonfeld, Frank; Braun, Matthias; Menz, Gunter

2010-12-01

Long-term change detection often implies the challenge of incorporating multitemporal data from different sensors. Most of the conventional change detection algorithms are designed for bi-temporal datasets from the same sensors detecting only the existence of changes. The labeling of change areas remains a difficult task. To overcome such drawbacks, much attention has been given lately to algorithms arising from machine learning, such as Support Vector Machines (SVMs). While SVMs have been applied successfully for land cover classifications, the exploitation of this approach for change detection is still in its infancy. Few studies have already proven the applicability of SVMs for bi- and multitemporal change detection using data from one sensor only. In this paper we demonstrate the application of SVM for multitemporal and -sensor change detection. Our study site covers lignite open pit mining areas in the German state North Rhine-Westphalia. The dataset consists of bi-temporal Landsat data and multi-temporal ERS SAR data covering two time slots (2001 and 2009). The SVM is conducted using the IDL program imageSVM. Change is deduced from one time slot to the next resulting in two change maps. In contrast to change detection, which is based on post-classification comparison, change detection is seen here as a specific classification problem. Thus, changes are directly classified from a layer-stack of the two years. To reduce the number of change classes, we created a change mask using the magnitude of Change Vector Analysis (CVA). Training data were selected for different change classes (e.g. forest to mining or mining to agriculture) as well as for the no-change classes (e.g. agriculture). Subsequently, they were divided in two independent sets for training the SVMs and accuracy assessment, respectively. Our study shows the applicability of SVMs to classify changes via SVMs. The proposed method yielded a change map of reclaimed and active mines. The use of ERS SAR data, however, did not add to the accuracy compared to Landsat data only. A great advantage compared to other change detection approaches are the labeled change maps, which are a direct output of the methodology. Our approach also overcomes the drawback of post-classification comparison, namely the propagation of classification inaccuracies.

An integrative machine learning strategy for improved prediction of essential genes in Escherichia coli metabolism using flux-coupled features.

PubMed

Nandi, Sutanu; Subramanian, Abhishek; Sarkar, Ram Rup

2017-07-25

Prediction of essential genes helps to identify a minimal set of genes that are absolutely required for the appropriate functioning and survival of a cell. The available machine learning techniques for essential gene prediction have inherent problems, like imbalanced provision of training datasets, biased choice of the best model for a given balanced dataset, choice of a complex machine learning algorithm, and data-based automated selection of biologically relevant features for classification. Here, we propose a simple support vector machine-based learning strategy for the prediction of essential genes in Escherichia coli K-12 MG1655 metabolism that integrates a non-conventional combination of an appropriate sample balanced training set, a unique organism-specific genotype, phenotype attributes that characterize essential genes, and optimal parameters of the learning algorithm to generate the best machine learning model (the model with the highest accuracy among all the models trained for different sample training sets). For the first time, we also introduce flux-coupled metabolic subnetwork-based features for enhancing the classification performance. Our strategy proves to be superior as compared to previous SVM-based strategies in obtaining a biologically relevant classification of genes with high sensitivity and specificity. This methodology was also trained with datasets of other recent supervised classification techniques for essential gene classification and tested using reported test datasets. The testing accuracy was always high as compared to the known techniques, proving that our method outperforms known methods. Observations from our study indicate that essential genes are conserved among homologous bacterial species, demonstrate high codon usage bias, GC content and gene expression, and predominantly possess a tendency to form physiological flux modules in metabolism.

Hyperspectral imaging with wavelet transform for classification of colon tissue biopsy samples

NASA Astrophysics Data System (ADS)

Masood, Khalid

2008-08-01

Automatic classification of medical images is a part of our computerised medical imaging programme to support the pathologists in their diagnosis. Hyperspectral data has found its applications in medical imagery. Its usage is increasing significantly in biopsy analysis of medical images. In this paper, we present a histopathological analysis for the classification of colon biopsy samples into benign and malignant classes. The proposed study is based on comparison between 3D spectral/spatial analysis and 2D spatial analysis. Wavelet textural features in the wavelet domain are used in both these approaches for classification of colon biopsy samples. Experimental results indicate that the incorporation of wavelet textural features using a support vector machine, in 2D spatial analysis, achieve best classification accuracy.

Hierarchical vs non-hierarchical audio indexation and classification for video genres

NASA Astrophysics Data System (ADS)

Dammak, Nouha; BenAyed, Yassine

2018-04-01

In this paper, Support Vector Machines (SVMs) are used for segmenting and indexing video genres based on only audio features extracted at block level, which has a prominent asset by capturing local temporal information. The main contribution of our study is to show the wide effect on the classification accuracies while using an hierarchical categorization structure based on Mel Frequency Cepstral Coefficients (MFCC) audio descriptor. In fact, the classification consists in three common video genres: sports videos, music clips and news scenes. The sub-classification may divide each genre into several multi-speaker and multi-dialect sub-genres. The validation of this approach was carried out on over 360 minutes of video span yielding a classification accuracy of over 99%.

Fusion of multiple quadratic penalty function support vector machines (QPFSVM) for automated sea mine detection and classification

NASA Astrophysics Data System (ADS)

Dobeck, Gerald J.; Cobb, J. Tory

2002-08-01

The high-resolution sonar is one of the principal sensors used by the Navy to detect and classify sea mines in minehunting operations. For such sonar systems, substantial effort has been devoted to the development of automated detection and classification (D/C) algorithms. These have been spurred by several factors including (1) aids for operators to reduce work overload, (2) more optimal use of all available data, and (3) the introduction of unmanned minehunting systems. The environments where sea mines are typically laid (harbor areas, shipping lanes, and the littorals) give rise to many false alarms caused by natural, biologic, and man-made clutter. The objective of the automated D/C algorithms is to eliminate most of these false alarms while still maintaining a very high probability of mine detection and classification (PdPc). In recent years, the benefits of fusing the outputs of multiple D/C algorithms have been studied. We refer to this as Algorithm Fusion. The results have been remarkable, including reliable robustness to new environments. The Quadratic Penalty Function Support Vector Machine (QPFSVM) algorithm to aid in the automated detection and classification of sea mines is introduced in this paper. The QPFSVM algorithm is easy to train, simple to implement, and robust to feature space dimension. Outputs of successive SVM algorithms are cascaded in stages (fused) to improve the Probability of Classification (Pc) and reduce the number of false alarms. Even though our experience has been gained in the area of sea mine detection and classification, the principles described herein are general and can be applied to fusion of any D/C problem (e.g., automated medical diagnosis or automatic target recognition for ballistic missile defense).

Supervised DNA Barcodes species classification: analysis, comparisons and results

PubMed Central

2014-01-01

Background Specific fragments, coming from short portions of DNA (e.g., mitochondrial, nuclear, and plastid sequences), have been defined as DNA Barcode and can be used as markers for organisms of the main life kingdoms. Species classification with DNA Barcode sequences has been proven effective on different organisms. Indeed, specific gene regions have been identified as Barcode: COI in animals, rbcL and matK in plants, and ITS in fungi. The classification problem assigns an unknown specimen to a known species by analyzing its Barcode. This task has to be supported with reliable methods and algorithms. Methods In this work the efficacy of supervised machine learning methods to classify species with DNA Barcode sequences is shown. The Weka software suite, which includes a collection of supervised classification methods, is adopted to address the task of DNA Barcode analysis. Classifier families are tested on synthetic and empirical datasets belonging to the animal, fungus, and plant kingdoms. In particular, the function-based method Support Vector Machines (SVM), the rule-based RIPPER, the decision tree C4.5, and the Naïve Bayes method are considered. Additionally, the classification results are compared with respect to ad-hoc and well-established DNA Barcode classification methods. Results A software that converts the DNA Barcode FASTA sequences to the Weka format is released, to adapt different input formats and to allow the execution of the classification procedure. The analysis of results on synthetic and real datasets shows that SVM and Naïve Bayes outperform on average the other considered classifiers, although they do not provide a human interpretable classification model. Rule-based methods have slightly inferior classification performances, but deliver the species specific positions and nucleotide assignments. On synthetic data the supervised machine learning methods obtain superior classification performances with respect to the traditional DNA Barcode classification methods. On empirical data their classification performances are at a comparable level to the other methods. Conclusions The classification analysis shows that supervised machine learning methods are promising candidates for handling with success the DNA Barcoding species classification problem, obtaining excellent performances. To conclude, a powerful tool to perform species identification is now available to the DNA Barcoding community. PMID:24721333

Online Artifact Removal for Brain-Computer Interfaces Using Support Vector Machines and Blind Source Separation

PubMed Central

Halder, Sebastian; Bensch, Michael; Mellinger, Jürgen; Bogdan, Martin; Kübler, Andrea; Birbaumer, Niels; Rosenstiel, Wolfgang

2007-01-01

We propose a combination of blind source separation (BSS) and independent component analysis (ICA) (signal decomposition into artifacts and nonartifacts) with support vector machines (SVMs) (automatic classification) that are designed for online usage. In order to select a suitable BSS/ICA method, three ICA algorithms (JADE, Infomax, and FastICA) and one BSS algorithm (AMUSE) are evaluated to determine their ability to isolate electromyographic (EMG) and electrooculographic (EOG) artifacts into individual components. An implementation of the selected BSS/ICA method with SVMs trained to classify EMG and EOG artifacts, which enables the usage of the method as a filter in measurements with online feedback, is described. This filter is evaluated on three BCI datasets as a proof-of-concept of the method. PMID:18288259

Online artifact removal for brain-computer interfaces using support vector machines and blind source separation.

PubMed

Halder, Sebastian; Bensch, Michael; Mellinger, Jürgen; Bogdan, Martin; Kübler, Andrea; Birbaumer, Niels; Rosenstiel, Wolfgang

2007-01-01

We propose a combination of blind source separation (BSS) and independent component analysis (ICA) (signal decomposition into artifacts and nonartifacts) with support vector machines (SVMs) (automatic classification) that are designed for online usage. In order to select a suitable BSS/ICA method, three ICA algorithms (JADE, Infomax, and FastICA) and one BSS algorithm (AMUSE) are evaluated to determine their ability to isolate electromyographic (EMG) and electrooculographic (EOG) artifacts into individual components. An implementation of the selected BSS/ICA method with SVMs trained to classify EMG and EOG artifacts, which enables the usage of the method as a filter in measurements with online feedback, is described. This filter is evaluated on three BCI datasets as a proof-of-concept of the method.

A Scatter-Based Prototype Framework and Multi-Class Extension of Support Vector Machines

PubMed Central

Jenssen, Robert; Kloft, Marius; Zien, Alexander; Sonnenburg, Sören; Müller, Klaus-Robert

2012-01-01

We provide a novel interpretation of the dual of support vector machines (SVMs) in terms of scatter with respect to class prototypes and their mean. As a key contribution, we extend this framework to multiple classes, providing a new joint Scatter SVM algorithm, at the level of its binary counterpart in the number of optimization variables. This enables us to implement computationally efficient solvers based on sequential minimal and chunking optimization. As a further contribution, the primal problem formulation is developed in terms of regularized risk minimization and the hinge loss, revealing the score function to be used in the actual classification of test patterns. We investigate Scatter SVM properties related to generalization ability, computational efficiency, sparsity and sensitivity maps, and report promising results. PMID:23118845

Classifying injury narratives of large administrative databases for surveillance-A practical approach combining machine learning ensembles and human review.

PubMed

Marucci-Wellman, Helen R; Corns, Helen L; Lehto, Mark R

2017-01-01

Injury narratives are now available real time and include useful information for injury surveillance and prevention. However, manual classification of the cause or events leading to injury found in large batches of narratives, such as workers compensation claims databases, can be prohibitive. In this study we compare the utility of four machine learning algorithms (Naïve Bayes, Single word and Bi-gram models, Support Vector Machine and Logistic Regression) for classifying narratives into Bureau of Labor Statistics Occupational Injury and Illness event leading to injury classifications for a large workers compensation database. These algorithms are known to do well classifying narrative text and are fairly easy to implement with off-the-shelf software packages such as Python. We propose human-machine learning ensemble approaches which maximize the power and accuracy of the algorithms for machine-assigned codes and allow for strategic filtering of rare, emerging or ambiguous narratives for manual review. We compare human-machine approaches based on filtering on the prediction strength of the classifier vs. agreement between algorithms. Regularized Logistic Regression (LR) was the best performing algorithm alone. Using this algorithm and filtering out the bottom 30% of predictions for manual review resulted in high accuracy (overall sensitivity/positive predictive value of 0.89) of the final machine-human coded dataset. The best pairings of algorithms included Naïve Bayes with Support Vector Machine whereby the triple ensemble NB SW =NB BI-GRAM =SVM had very high performance (0.93 overall sensitivity/positive predictive value and high accuracy (i.e. high sensitivity and positive predictive values)) across both large and small categories leaving 41% of the narratives for manual review. Integrating LR into this ensemble mix improved performance only slightly. For large administrative datasets we propose incorporation of methods based on human-machine pairings such as we have done here, utilizing readily-available off-the-shelf machine learning techniques and resulting in only a fraction of narratives that require manual review. Human-machine ensemble methods are likely to improve performance over total manual coding. Copyright © 2016 The Authors. Published by Elsevier Ltd.. All rights reserved.

An efficient ensemble learning method for gene microarray classification.

PubMed

Osareh, Alireza; Shadgar, Bita

2013-01-01

The gene microarray analysis and classification have demonstrated an effective way for the effective diagnosis of diseases and cancers. However, it has been also revealed that the basic classification techniques have intrinsic drawbacks in achieving accurate gene classification and cancer diagnosis. On the other hand, classifier ensembles have received increasing attention in various applications. Here, we address the gene classification issue using RotBoost ensemble methodology. This method is a combination of Rotation Forest and AdaBoost techniques which in turn preserve both desirable features of an ensemble architecture, that is, accuracy and diversity. To select a concise subset of informative genes, 5 different feature selection algorithms are considered. To assess the efficiency of the RotBoost, other nonensemble/ensemble techniques including Decision Trees, Support Vector Machines, Rotation Forest, AdaBoost, and Bagging are also deployed. Experimental results have revealed that the combination of the fast correlation-based feature selection method with ICA-based RotBoost ensemble is highly effective for gene classification. In fact, the proposed method can create ensemble classifiers which outperform not only the classifiers produced by the conventional machine learning but also the classifiers generated by two widely used conventional ensemble learning methods, that is, Bagging and AdaBoost.

Integrative image segmentation optimization and machine learning approach for high quality land-use and land-cover mapping using multisource remote sensing data

NASA Astrophysics Data System (ADS)

Gibril, Mohamed Barakat A.; Idrees, Mohammed Oludare; Yao, Kouame; Shafri, Helmi Zulhaidi Mohd

2018-01-01

The growing use of optimization for geographic object-based image analysis and the possibility to derive a wide range of information about the image in textual form makes machine learning (data mining) a versatile tool for information extraction from multiple data sources. This paper presents application of data mining for land-cover classification by fusing SPOT-6, RADARSAT-2, and derived dataset. First, the images and other derived indices (normalized difference vegetation index, normalized difference water index, and soil adjusted vegetation index) were combined and subjected to segmentation process with optimal segmentation parameters obtained using combination of spatial and Taguchi statistical optimization. The image objects, which carry all the attributes of the input datasets, were extracted and related to the target land-cover classes through data mining algorithms (decision tree) for classification. To evaluate the performance, the result was compared with two nonparametric classifiers: support vector machine (SVM) and random forest (RF). Furthermore, the decision tree classification result was evaluated against six unoptimized trials segmented using arbitrary parameter combinations. The result shows that the optimized process produces better land-use land-cover classification with overall classification accuracy of 91.79%, 87.25%, and 88.69% for SVM and RF, respectively, while the results of the six unoptimized classifications yield overall accuracy between 84.44% and 88.08%. Higher accuracy of the optimized data mining classification approach compared to the unoptimized results indicates that the optimization process has significant impact on the classification quality.

Unified framework for triaxial accelerometer-based fall event detection and classification using cumulants and hierarchical decision tree classifier.

PubMed

Kambhampati, Satya Samyukta; Singh, Vishal; Manikandan, M Sabarimalai; Ramkumar, Barathram

2015-08-01

In this Letter, the authors present a unified framework for fall event detection and classification using the cumulants extracted from the acceleration (ACC) signals acquired using a single waist-mounted triaxial accelerometer. The main objective of this Letter is to find suitable representative cumulants and classifiers in effectively detecting and classifying different types of fall and non-fall events. It was discovered that the first level of the proposed hierarchical decision tree algorithm implements fall detection using fifth-order cumulants and support vector machine (SVM) classifier. In the second level, the fall event classification algorithm uses the fifth-order cumulants and SVM. Finally, human activity classification is performed using the second-order cumulants and SVM. The detection and classification results are compared with those of the decision tree, naive Bayes, multilayer perceptron and SVM classifiers with different types of time-domain features including the second-, third-, fourth- and fifth-order cumulants and the signal magnitude vector and signal magnitude area. The experimental results demonstrate that the second- and fifth-order cumulant features and SVM classifier can achieve optimal detection and classification rates of above 95%, as well as the lowest false alarm rate of 1.03%.

Comparison of Random Forest, k-Nearest Neighbor, and Support Vector Machine Classifiers for Land Cover Classification Using Sentinel-2 Imagery

PubMed Central

Thanh Noi, Phan; Kappas, Martin

2017-01-01

In previous classification studies, three non-parametric classifiers, Random Forest (RF), k-Nearest Neighbor (kNN), and Support Vector Machine (SVM), were reported as the foremost classifiers at producing high accuracies. However, only a few studies have compared the performances of these classifiers with different training sample sizes for the same remote sensing images, particularly the Sentinel-2 Multispectral Imager (MSI). In this study, we examined and compared the performances of the RF, kNN, and SVM classifiers for land use/cover classification using Sentinel-2 image data. An area of 30 × 30 km2 within the Red River Delta of Vietnam with six land use/cover types was classified using 14 different training sample sizes, including balanced and imbalanced, from 50 to over 1250 pixels/class. All classification results showed a high overall accuracy (OA) ranging from 90% to 95%. Among the three classifiers and 14 sub-datasets, SVM produced the highest OA with the least sensitivity to the training sample sizes, followed consecutively by RF and kNN. In relation to the sample size, all three classifiers showed a similar and high OA (over 93.85%) when the training sample size was large enough, i.e., greater than 750 pixels/class or representing an area of approximately 0.25% of the total study area. The high accuracy was achieved with both imbalanced and balanced datasets. PMID:29271909

Comparison of Random Forest, k-Nearest Neighbor, and Support Vector Machine Classifiers for Land Cover Classification Using Sentinel-2 Imagery.

PubMed

Thanh Noi, Phan; Kappas, Martin

2017-12-22

In previous classification studies, three non-parametric classifiers, Random Forest (RF), k-Nearest Neighbor (kNN), and Support Vector Machine (SVM), were reported as the foremost classifiers at producing high accuracies. However, only a few studies have compared the performances of these classifiers with different training sample sizes for the same remote sensing images, particularly the Sentinel-2 Multispectral Imager (MSI). In this study, we examined and compared the performances of the RF, kNN, and SVM classifiers for land use/cover classification using Sentinel-2 image data. An area of 30 × 30 km² within the Red River Delta of Vietnam with six land use/cover types was classified using 14 different training sample sizes, including balanced and imbalanced, from 50 to over 1250 pixels/class. All classification results showed a high overall accuracy (OA) ranging from 90% to 95%. Among the three classifiers and 14 sub-datasets, SVM produced the highest OA with the least sensitivity to the training sample sizes, followed consecutively by RF and kNN. In relation to the sample size, all three classifiers showed a similar and high OA (over 93.85%) when the training sample size was large enough, i.e., greater than 750 pixels/class or representing an area of approximately 0.25% of the total study area. The high accuracy was achieved with both imbalanced and balanced datasets.

Using methods from the data mining and machine learning literature for disease classification and prediction: A case study examining classification of heart failure sub-types

PubMed Central

Austin, Peter C.; Tu, Jack V.; Ho, Jennifer E.; Levy, Daniel; Lee, Douglas S.

2014-01-01

Objective Physicians classify patients into those with or without a specific disease. Furthermore, there is often interest in classifying patients according to disease etiology or subtype. Classification trees are frequently used to classify patients according to the presence or absence of a disease. However, classification trees can suffer from limited accuracy. In the data-mining and machine learning literature, alternate classification schemes have been developed. These include bootstrap aggregation (bagging), boosting, random forests, and support vector machines. Study design and Setting We compared the performance of these classification methods with those of conventional classification trees to classify patients with heart failure according to the following sub-types: heart failure with preserved ejection fraction (HFPEF) vs. heart failure with reduced ejection fraction (HFREF). We also compared the ability of these methods to predict the probability of the presence of HFPEF with that of conventional logistic regression. Results We found that modern, flexible tree-based methods from the data mining literature offer substantial improvement in prediction and classification of heart failure sub-type compared to conventional classification and regression trees. However, conventional logistic regression had superior performance for predicting the probability of the presence of HFPEF compared to the methods proposed in the data mining literature. Conclusion The use of tree-based methods offers superior performance over conventional classification and regression trees for predicting and classifying heart failure subtypes in a population-based sample of patients from Ontario. However, these methods do not offer substantial improvements over logistic regression for predicting the presence of HFPEF. PMID:23384592

Monitoring Hitting Load in Tennis Using Inertial Sensors and Machine Learning.

PubMed

Whiteside, David; Cant, Olivia; Connolly, Molly; Reid, Machar

2017-10-01

Quantifying external workload is fundamental to training prescription in sport. In tennis, global positioning data are imprecise and fail to capture hitting loads. The current gold standard (manual notation) is time intensive and often not possible given players' heavy travel schedules. To develop an automated stroke-classification system to help quantify hitting load in tennis. Nineteen athletes wore an inertial measurement unit (IMU) on their wrist during 66 video-recorded training sessions. Video footage was manually notated such that known shot type (serve, rally forehand, slice forehand, forehand volley, rally backhand, slice backhand, backhand volley, smash, or false positive) was associated with the corresponding IMU data for 28,582 shots. Six types of machine-learning models were then constructed to classify true shot type from the IMU signals. Across 10-fold cross-validation, a cubic-kernel support vector machine classified binned shots (overhead, forehand, or backhand) with an accuracy of 97.4%. A second cubic-kernel support vector machine achieved 93.2% accuracy when classifying all 9 shot types. With a view to monitoring external load, the combination of miniature inertial sensors and machine learning offers a practical and automated method of quantifying shot counts and discriminating shot types in elite tennis players.

Machinery Bearing Fault Diagnosis Using Variational Mode Decomposition and Support Vector Machine as a Classifier

NASA Astrophysics Data System (ADS)

Rama Krishna, K.; Ramachandran, K. I.

2018-02-01

Crack propagation is a major cause of failure in rotating machines. It adversely affects the productivity, safety, and the machining quality. Hence, detecting the crack’s severity accurately is imperative for the predictive maintenance of such machines. Fault diagnosis is an established concept in identifying the faults, for observing the non-linear behaviour of the vibration signals at various operating conditions. In this work, we find the classification efficiencies for both original and the reconstructed vibrational signals. The reconstructed signals are obtained using Variational Mode Decomposition (VMD), by splitting the original signal into three intrinsic mode functional components and framing them accordingly. Feature extraction, feature selection and feature classification are the three phases in obtaining the classification efficiencies. All the statistical features from the original signals and reconstructed signals are found out in feature extraction process individually. A few statistical parameters are selected in feature selection process and are classified using the SVM classifier. The obtained results show the best parameters and appropriate kernel in SVM classifier for detecting the faults in bearings. Hence, we conclude that better results were obtained by VMD and SVM process over normal process using SVM. This is owing to denoising and filtering the raw vibrational signals.

Using Neural Networks to Classify Digitized Images of Galaxies

NASA Astrophysics Data System (ADS)

Goderya, S. N.; McGuire, P. C.

2000-12-01

Automated classification of Galaxies into Hubble types is of paramount importance to study the large scale structure of the Universe, particularly as survey projects like the Sloan Digital Sky Survey complete their data acquisition of one million galaxies. At present it is not possible to find robust and efficient artificial intelligence based galaxy classifiers. In this study we will summarize progress made in the development of automated galaxy classifiers using neural networks as machine learning tools. We explore the Bayesian linear algorithm, the higher order probabilistic network, the multilayer perceptron neural network and Support Vector Machine Classifier. The performance of any machine classifier is dependant on the quality of the parameters that characterize the different groups of galaxies. Our effort is to develop geometric and invariant moment based parameters as input to the machine classifiers instead of the raw pixel data. Such an approach reduces the dimensionality of the classifier considerably, and removes the effects of scaling and rotation, and makes it easier to solve for the unknown parameters in the galaxy classifier. To judge the quality of training and classification we develop the concept of Mathews coefficients for the galaxy classification community. Mathews coefficients are single numbers that quantify classifier performance even with unequal prior probabilities of the classes.

A machine learning model with human cognitive biases capable of learning from small and biased datasets.

PubMed

Taniguchi, Hidetaka; Sato, Hiroshi; Shirakawa, Tomohiro

2018-05-09

Human learners can generalize a new concept from a small number of samples. In contrast, conventional machine learning methods require large amounts of data to address the same types of problems. Humans have cognitive biases that promote fast learning. Here, we developed a method to reduce the gap between human beings and machines in this type of inference by utilizing cognitive biases. We implemented a human cognitive model into machine learning algorithms and compared their performance with the currently most popular methods, naïve Bayes, support vector machine, neural networks, logistic regression and random forests. We focused on the task of spam classification, which has been studied for a long time in the field of machine learning and often requires a large amount of data to obtain high accuracy. Our models achieved superior performance with small and biased samples in comparison with other representative machine learning methods.

Phenotyping: Using Machine Learning for Improved Pairwise Genotype Classification Based on Root Traits

PubMed Central

Zhao, Jiangsan; Bodner, Gernot; Rewald, Boris

2016-01-01

Phenotyping local crop cultivars is becoming more and more important, as they are an important genetic source for breeding – especially in regard to inherent root system architectures. Machine learning algorithms are promising tools to assist in the analysis of complex data sets; novel approaches are need to apply them on root phenotyping data of mature plants. A greenhouse experiment was conducted in large, sand-filled columns to differentiate 16 European Pisum sativum cultivars based on 36 manually derived root traits. Through combining random forest and support vector machine models, machine learning algorithms were successfully used for unbiased identification of most distinguishing root traits and subsequent pairwise cultivar differentiation. Up to 86% of pea cultivar pairs could be distinguished based on top five important root traits (Timp5) – Timp5 differed widely between cultivar pairs. Selecting top important root traits (Timp) provided a significant improved classification compared to using all available traits or randomly selected trait sets. The most frequent Timp of mature pea cultivars was total surface area of lateral roots originating from tap root segments at 0–5 cm depth. The high classification rate implies that culturing did not lead to a major loss of variability in root system architecture in the studied pea cultivars. Our results illustrate the potential of machine learning approaches for unbiased (root) trait selection and cultivar classification based on rather small, complex phenotypic data sets derived from pot experiments. Powerful statistical approaches are essential to make use of the increasing amount of (root) phenotyping information, integrating the complex trait sets describing crop cultivars. PMID:27999587

Accurate Identification of Cancerlectins through Hybrid Machine Learning Technology.

PubMed

Zhang, Jieru; Ju, Ying; Lu, Huijuan; Xuan, Ping; Zou, Quan

2016-01-01

Cancerlectins are cancer-related proteins that function as lectins. They have been identified through computational identification techniques, but these techniques have sometimes failed to identify proteins because of sequence diversity among the cancerlectins. Advanced machine learning identification methods, such as support vector machine and basic sequence features (n-gram), have also been used to identify cancerlectins. In this study, various protein fingerprint features and advanced classifiers, including ensemble learning techniques, were utilized to identify this group of proteins. We improved the prediction accuracy of the original feature extraction methods and classification algorithms by more than 10% on average. Our work provides a basis for the computational identification of cancerlectins and reveals the power of hybrid machine learning techniques in computational proteomics.

Privacy-Preserving Evaluation of Generalization Error and Its Application to Model and Attribute Selection

NASA Astrophysics Data System (ADS)

Sakuma, Jun; Wright, Rebecca N.

Privacy-preserving classification is the task of learning or training a classifier on the union of privately distributed datasets without sharing the datasets. The emphasis of existing studies in privacy-preserving classification has primarily been put on the design of privacy-preserving versions of particular data mining algorithms, However, in classification problems, preprocessing and postprocessing— such as model selection or attribute selection—play a prominent role in achieving higher classification accuracy. In this paper, we show generalization error of classifiers in privacy-preserving classification can be securely evaluated without sharing prediction results. Our main technical contribution is a new generalized Hamming distance protocol that is universally applicable to preprocessing and postprocessing of various privacy-preserving classification problems, such as model selection in support vector machine and attribute selection in naive Bayes classification.

Machine learning and dyslexia: Classification of individual structural neuro-imaging scans of students with and without dyslexia.

PubMed

Tamboer, P; Vorst, H C M; Ghebreab, S; Scholte, H S

2016-01-01

Meta-analytic studies suggest that dyslexia is characterized by subtle and spatially distributed variations in brain anatomy, although many variations failed to be significant after corrections of multiple comparisons. To circumvent issues of significance which are characteristic for conventional analysis techniques, and to provide predictive value, we applied a machine learning technique--support vector machine--to differentiate between subjects with and without dyslexia. In a sample of 22 students with dyslexia (20 women) and 27 students without dyslexia (25 women) (18-21 years), a classification performance of 80% (p < 0.001; d-prime = 1.67) was achieved on the basis of differences in gray matter (sensitivity 82%, specificity 78%). The voxels that were most reliable for classification were found in the left occipital fusiform gyrus (LOFG), in the right occipital fusiform gyrus (ROFG), and in the left inferior parietal lobule (LIPL). Additionally, we found that classification certainty (e.g. the percentage of times a subject was correctly classified) correlated with severity of dyslexia (r = 0.47). Furthermore, various significant correlations were found between the three anatomical regions and behavioural measures of spelling, phonology and whole-word-reading. No correlations were found with behavioural measures of short-term memory and visual/attentional confusion. These data indicate that the LOFG, ROFG and the LIPL are neuro-endophenotype and potentially biomarkers for types of dyslexia related to reading, spelling and phonology. In a second and independent sample of 876 young adults of a general population, the trained classifier of the first sample was tested, resulting in a classification performance of 59% (p = 0.07; d-prime = 0.65). This decline in classification performance resulted from a large percentage of false alarms. This study provided support for the use of machine learning in anatomical brain imaging.

Breast cancer risk assessment and diagnosis model using fuzzy support vector machine based expert system

NASA Astrophysics Data System (ADS)

Dheeba, J.; Jaya, T.; Singh, N. Albert

2017-09-01

Classification of cancerous masses is a challenging task in many computerised detection systems. Cancerous masses are difficult to detect because these masses are obscured and subtle in mammograms. This paper investigates an intelligent classifier - fuzzy support vector machine (FSVM) applied to classify the tissues containing masses on mammograms for breast cancer diagnosis. The algorithm utilises texture features extracted using Laws texture energy measures and a FSVM to classify the suspicious masses. The new FSVM treats every feature as both normal and abnormal samples, but with different membership. By this way, the new FSVM have more generalisation ability to classify the masses in mammograms. The classifier analysed 219 clinical mammograms collected from breast cancer screening laboratory. The tests made on the real clinical mammograms shows that the proposed detection system has better discriminating power than the conventional support vector machine. With the best combination of FSVM and Laws texture features, the area under the Receiver operating characteristic curve reached .95, which corresponds to a sensitivity of 93.27% with a specificity of 87.17%. The results suggest that detecting masses using FSVM contribute to computer-aided detection of breast cancer and as a decision support system for radiologists.

[Machine Learning-based Prediction of Seizure-inducing Action as an Adverse Drug Effect].

PubMed

Gao, Mengxuan; Sato, Motoshige; Ikegaya, Yuji

2018-01-01

　During the preclinical research period of drug development, animal testing is widely used to help screen out a drug's dangerous side effects. However, it remains difficult to predict side effects within the central nervous system. Here, we introduce a machine learning-based in vitro system designed to detect seizure-inducing side effects before clinical trial. We recorded local field potentials from the CA1 alveus in acute mouse neocortico-hippocampal slices that were bath-perfused with each of 14 different drugs, and at 5 different concentrations of each drug. For each of these experimental conditions, we collected seizure-like neuronal activity and merged their waveforms as one graphic image, which was further converted into a feature vector using Caffe, an open framework for deep learning. In the space of the first two principal components, the support vector machine completely separated the vectors (i.e., doses of individual drugs) that induced seizure-like events, and identified diphenhydramine, enoxacin, strychnine and theophylline as "seizure-inducing" drugs, which have indeed been reported to induce seizures in clinical situations. Thus, this artificial intelligence-based classification may provide a new platform to pre-clinically detect seizure-inducing side effects of drugs.

Machine learning-based prediction of adverse drug effects: An example of seizure-inducing compounds.

PubMed

Gao, Mengxuan; Igata, Hideyoshi; Takeuchi, Aoi; Sato, Kaoru; Ikegaya, Yuji

2017-02-01

Various biological factors have been implicated in convulsive seizures, involving side effects of drugs. For the preclinical safety assessment of drug development, it is difficult to predict seizure-inducing side effects. Here, we introduced a machine learning-based in vitro system designed to detect seizure-inducing side effects. We recorded local field potentials from the CA1 alveus in acute mouse neocortico-hippocampal slices, while 14 drugs were bath-perfused at 5 different concentrations each. For each experimental condition, we collected seizure-like neuronal activity and merged their waveforms as one graphic image, which was further converted into a feature vector using Caffe, an open framework for deep learning. In the space of the first two principal components, the support vector machine completely separated the vectors (i.e., doses of individual drugs) that induced seizure-like events and identified diphenhydramine, enoxacin, strychnine and theophylline as "seizure-inducing" drugs, which indeed were reported to induce seizures in clinical situations. Thus, this artificial intelligence-based classification may provide a new platform to detect the seizure-inducing side effects of preclinical drugs. Copyright © 2017 The Authors. Production and hosting by Elsevier B.V. All rights reserved.

Fuzzy Nonlinear Proximal Support Vector Machine for Land Extraction Based on Remote Sensing Image

PubMed Central

Zhong, Xiaomei; Li, Jianping; Dou, Huacheng; Deng, Shijun; Wang, Guofei; Jiang, Yu; Wang, Yongjie; Zhou, Zebing; Wang, Li; Yan, Fei

2013-01-01

Currently, remote sensing technologies were widely employed in the dynamic monitoring of the land. This paper presented an algorithm named fuzzy nonlinear proximal support vector machine (FNPSVM) by basing on ETM+ remote sensing image. This algorithm is applied to extract various types of lands of the city Da’an in northern China. Two multi-category strategies, namely “one-against-one” and “one-against-rest” for this algorithm were described in detail and then compared. A fuzzy membership function was presented to reduce the effects of noises or outliers on the data samples. The approaches of feature extraction, feature selection, and several key parameter settings were also given. Numerous experiments were carried out to evaluate its performances including various accuracies (overall accuracies and kappa coefficient), stability, training speed, and classification speed. The FNPSVM classifier was compared to the other three classifiers including the maximum likelihood classifier (MLC), back propagation neural network (BPN), and the proximal support vector machine (PSVM) under different training conditions. The impacts of the selection of training samples, testing samples and features on the four classifiers were also evaluated in these experiments. PMID:23936016

A Real-Time Interference Monitoring Technique for GNSS Based on a Twin Support Vector Machine Method.

PubMed

Li, Wutao; Huang, Zhigang; Lang, Rongling; Qin, Honglei; Zhou, Kai; Cao, Yongbin

2016-03-04

Interferences can severely degrade the performance of Global Navigation Satellite System (GNSS) receivers. As the first step of GNSS any anti-interference measures, interference monitoring for GNSS is extremely essential and necessary. Since interference monitoring can be considered as a classification problem, a real-time interference monitoring technique based on Twin Support Vector Machine (TWSVM) is proposed in this paper. A TWSVM model is established, and TWSVM is solved by the Least Squares Twin Support Vector Machine (LSTWSVM) algorithm. The interference monitoring indicators are analyzed to extract features from the interfered GNSS signals. The experimental results show that the chosen observations can be used as the interference monitoring indicators. The interference monitoring performance of the proposed method is verified by using GPS L1 C/A code signal and being compared with that of standard SVM. The experimental results indicate that the TWSVM-based interference monitoring is much faster than the conventional SVM. Furthermore, the training time of TWSVM is on millisecond (ms) level and the monitoring time is on microsecond (μs) level, which make the proposed approach usable in practical interference monitoring applications.

A Real-Time Interference Monitoring Technique for GNSS Based on a Twin Support Vector Machine Method

PubMed Central

Li, Wutao; Huang, Zhigang; Lang, Rongling; Qin, Honglei; Zhou, Kai; Cao, Yongbin

2016-01-01

Interferences can severely degrade the performance of Global Navigation Satellite System (GNSS) receivers. As the first step of GNSS any anti-interference measures, interference monitoring for GNSS is extremely essential and necessary. Since interference monitoring can be considered as a classification problem, a real-time interference monitoring technique based on Twin Support Vector Machine (TWSVM) is proposed in this paper. A TWSVM model is established, and TWSVM is solved by the Least Squares Twin Support Vector Machine (LSTWSVM) algorithm. The interference monitoring indicators are analyzed to extract features from the interfered GNSS signals. The experimental results show that the chosen observations can be used as the interference monitoring indicators. The interference monitoring performance of the proposed method is verified by using GPS L1 C/A code signal and being compared with that of standard SVM. The experimental results indicate that the TWSVM-based interference monitoring is much faster than the conventional SVM. Furthermore, the training time of TWSVM is on millisecond (ms) level and the monitoring time is on microsecond (μs) level, which make the proposed approach usable in practical interference monitoring applications. PMID:26959020

Integration of spectral, spatial and morphometric data into lithological mapping: A comparison of different Machine Learning Algorithms in the Kurdistan Region, NE Iraq

NASA Astrophysics Data System (ADS)

Othman, Arsalan A.; Gloaguen, Richard

2017-09-01

Lithological mapping in mountainous regions is often impeded by limited accessibility due to relief. This study aims to evaluate (1) the performance of different supervised classification approaches using remote sensing data and (2) the use of additional information such as geomorphology. We exemplify the methodology in the Bardi-Zard area in NE Iraq, a part of the Zagros Fold - Thrust Belt, known for its chromite deposits. We highlighted the improvement of remote sensing geological classification by integrating geomorphic features and spatial information in the classification scheme. We performed a Maximum Likelihood (ML) classification method besides two Machine Learning Algorithms (MLA): Support Vector Machine (SVM) and Random Forest (RF) to allow the joint use of geomorphic features, Band Ratio (BR), Principal Component Analysis (PCA), spatial information (spatial coordinates) and multispectral data of the Advanced Space-borne Thermal Emission and Reflection radiometer (ASTER) satellite. The RF algorithm showed reliable results and discriminated serpentinite, talus and terrace deposits, red argillites with conglomerates and limestone, limy conglomerates and limestone conglomerates, tuffites interbedded with basic lavas, limestone and Metamorphosed limestone and reddish green shales. The best overall accuracy (∼80%) was achieved by Random Forest (RF) algorithms in the majority of the sixteen tested combination datasets.

Fast Query-Optimized Kernel-Machine Classification

NASA Technical Reports Server (NTRS)

Mazzoni, Dominic; DeCoste, Dennis

2004-01-01

A recently developed algorithm performs kernel-machine classification via incremental approximate nearest support vectors. The algorithm implements support-vector machines (SVMs) at speeds 10 to 100 times those attainable by use of conventional SVM algorithms. The algorithm offers potential benefits for classification of images, recognition of speech, recognition of handwriting, and diverse other applications in which there are requirements to discern patterns in large sets of data. SVMs constitute a subset of kernel machines (KMs), which have become popular as models for machine learning and, more specifically, for automated classification of input data on the basis of labeled training data. While similar in many ways to k-nearest-neighbors (k-NN) models and artificial neural networks (ANNs), SVMs tend to be more accurate. Using representations that scale only linearly in the numbers of training examples, while exploring nonlinear (kernelized) feature spaces that are exponentially larger than the original input dimensionality, KMs elegantly and practically overcome the classic curse of dimensionality. However, the price that one must pay for the power of KMs is that query-time complexity scales linearly with the number of training examples, making KMs often orders of magnitude more computationally expensive than are ANNs, decision trees, and other popular machine learning alternatives. The present algorithm treats an SVM classifier as a special form of a k-NN. The algorithm is based partly on an empirical observation that one can often achieve the same classification as that of an exact KM by using only small fraction of the nearest support vectors (SVs) of a query. The exact KM output is a weighted sum over the kernel values between the query and the SVs. In this algorithm, the KM output is approximated with a k-NN classifier, the output of which is a weighted sum only over the kernel values involving k selected SVs. Before query time, there are gathered statistics about how misleading the output of the k-NN model can be, relative to the outputs of the exact KM for a representative set of examples, for each possible k from 1 to the total number of SVs. From these statistics, there are derived upper and lower thresholds for each step k. These thresholds identify output levels for which the particular variant of the k-NN model already leans so strongly positively or negatively that a reversal in sign is unlikely, given the weaker SV neighbors still remaining. At query time, the partial output of each query is incrementally updated, stopping as soon as it exceeds the predetermined statistical thresholds of the current step. For an easy query, stopping can occur as early as step k = 1. For more difficult queries, stopping might not occur until nearly all SVs are touched. A key empirical observation is that this approach can tolerate very approximate nearest-neighbor orderings. In experiments, SVs and queries were projected to a subspace comprising the top few principal- component dimensions and neighbor orderings were computed in that subspace. This approach ensured that the overhead of the nearest-neighbor computations was insignificant, relative to that of the exact KM computation.

Application of machine learning using support vector machines for crater detection from Martian digital topography data

NASA Astrophysics Data System (ADS)

Salamunićcar, Goran; Lončarić, Sven

In our previous work, in order to extend the GT-57633 catalogue [PSS, 56 (15), 1992-2008] with still uncatalogued impact-craters, the following has been done [GRS, 48 (5), in press, doi:10.1109/TGRS.2009.2037750]: (1) the crater detection algorithm (CDA) based on digital elevation model (DEM) was developed; (2) using 1/128° MOLA data, this CDA proposed 414631 crater-candidates; (3) each crater-candidate was analyzed manually; and (4) 57592 were confirmed as correct detections. The resulting GT-115225 catalog is the significant result of this effort. However, to check such a large number of crater-candidates manually was a demanding task. This was the main motivation for work on improvement of the CDA in order to provide better classification of craters as true and false detections. To achieve this, we extended the CDA with the machine learning capability, using support vector machines (SVM). In the first step, the CDA (re)calculates numerous terrain morphometric attributes from DEM. For this purpose, already existing modules of the CDA from our previous work were reused in order to be capable to prepare these attributes. In addition, new attributes were introduced such as ellipse eccentricity and tilt. For machine learning purpose, the CDA is additionally extended to provide 2-D topography-profile and 3-D shape for each crater-candidate. The latter two are a performance problem because of the large number of crater-candidates in combination with the large number of attributes. As a solution, we developed a CDA architecture wherein it is possible to combine the SVM with a radial basis function (RBF) or any other kernel (for initial set of attributes), with the SVM with linear kernel (for the cases when 2-D and 3-D data are included as well). Another challenge is that, in addition to diversity of possible crater types, there are numerous morphological differences between the smallest (mostly very circular bowl-shaped craters) and the largest (multi-ring) impact craters. As a solution to this problem, the CDA classifies crater-candidates according to their diameter into 7 groups (D smaller/larger then 2km, 4km, 8km, 16km, 32km and 64km), and for each group uses separate SVMs for training and prediction. For implementation of the machine-learning part and integration with the rest of the CDA, we used C.-J. Lin's et al. [http://www.csie.ntu.edu.tw/˜cjlin/] LIBSVM (A Library for Support Vector Machines) and LIBLINEAR (A Library for Large Linear Classification) libraries. According to the initial evaluation, now the CDA provides much better classification of craters as true and false detections.

Optimization of breast mass classification using sequential forward floating selection (SFFS) and a support vector machine (SVM) model

PubMed Central

Tan, Maxine; Pu, Jiantao; Zheng, Bin

2014-01-01

Purpose: Improving radiologists’ performance in classification between malignant and benign breast lesions is important to increase cancer detection sensitivity and reduce false-positive recalls. For this purpose, developing computer-aided diagnosis (CAD) schemes has been attracting research interest in recent years. In this study, we investigated a new feature selection method for the task of breast mass classification. Methods: We initially computed 181 image features based on mass shape, spiculation, contrast, presence of fat or calcifications, texture, isodensity, and other morphological features. From this large image feature pool, we used a sequential forward floating selection (SFFS)-based feature selection method to select relevant features, and analyzed their performance using a support vector machine (SVM) model trained for the classification task. On a database of 600 benign and 600 malignant mass regions of interest (ROIs), we performed the study using a ten-fold cross-validation method. Feature selection and optimization of the SVM parameters were conducted on the training subsets only. Results: The area under the receiver operating characteristic curve (AUC) = 0.805±0.012 was obtained for the classification task. The results also showed that the most frequently-selected features by the SFFS-based algorithm in 10-fold iterations were those related to mass shape, isodensity and presence of fat, which are consistent with the image features frequently used by radiologists in the clinical environment for mass classification. The study also indicated that accurately computing mass spiculation features from the projection mammograms was difficult, and failed to perform well for the mass classification task due to tissue overlap within the benign mass regions. Conclusions: In conclusion, this comprehensive feature analysis study provided new and valuable information for optimizing computerized mass classification schemes that may have potential to be useful as a “second reader” in future clinical practice. PMID:24664267

Automatic breast density classification using a convolutional neural network architecture search procedure

NASA Astrophysics Data System (ADS)

Fonseca, Pablo; Mendoza, Julio; Wainer, Jacques; Ferrer, Jose; Pinto, Joseph; Guerrero, Jorge; Castaneda, Benjamin

2015-03-01

Breast parenchymal density is considered a strong indicator of breast cancer risk and therefore useful for preventive tasks. Measurement of breast density is often qualitative and requires the subjective judgment of radiologists. Here we explore an automatic breast composition classification workflow based on convolutional neural networks for feature extraction in combination with a support vector machines classifier. This is compared to the assessments of seven experienced radiologists. The experiments yielded an average kappa value of 0.58 when using the mode of the radiologists' classifications as ground truth. Individual radiologist performance against this ground truth yielded kappa values between 0.56 and 0.79.

SVMs for Vibration-Based Terrain Classification

NASA Astrophysics Data System (ADS)

Weiss, Christian; Stark, Matthias; Zell, Andreas

When an outdoor mobile robot traverses different types of ground surfaces, different types of vibrations are induced in the body of the robot. These vibrations can be used to learn a discrimination between different surfaces and to classify the current terrain. Recently, we presented a method that uses Support Vector Machines for classification, and we showed results on data collected with a hand-pulled cart. In this paper, we show that our approach also works well on an outdoor robot. Furthermore, we more closely investigate in which direction the vibration should be measured. Finally, we present a simple but effective method to improve the classification by combining measurements taken in multiple directions.

Superpixel-based spectral classification for the detection of head and neck cancer with hyperspectral imaging

NASA Astrophysics Data System (ADS)

Chung, Hyunkoo; Lu, Guolan; Tian, Zhiqiang; Wang, Dongsheng; Chen, Zhuo Georgia; Fei, Baowei

2016-03-01

Hyperspectral imaging (HSI) is an emerging imaging modality for medical applications. HSI acquires two dimensional images at various wavelengths. The combination of both spectral and spatial information provides quantitative information for cancer detection and diagnosis. This paper proposes using superpixels, principal component analysis (PCA), and support vector machine (SVM) to distinguish regions of tumor from healthy tissue. The classification method uses 2 principal components decomposed from hyperspectral images and obtains an average sensitivity of 93% and an average specificity of 85% for 11 mice. The hyperspectral imaging technology and classification method can have various applications in cancer research and management.

Steganalysis using logistic regression

NASA Astrophysics Data System (ADS)

Lubenko, Ivans; Ker, Andrew D.

2011-02-01

We advocate Logistic Regression (LR) as an alternative to the Support Vector Machine (SVM) classifiers commonly used in steganalysis. LR offers more information than traditional SVM methods - it estimates class probabilities as well as providing a simple classification - and can be adapted more easily and efficiently for multiclass problems. Like SVM, LR can be kernelised for nonlinear classification, and it shows comparable classification accuracy to SVM methods. This work is a case study, comparing accuracy and speed of SVM and LR classifiers in detection of LSB Matching and other related spatial-domain image steganography, through the state-of-art 686-dimensional SPAM feature set, in three image sets.

Effect of filtration of signals of brain activity on quality of recognition of brain activity patterns using artificial intelligence methods

NASA Astrophysics Data System (ADS)

Hramov, Alexander E.; Frolov, Nikita S.; Musatov, Vyachaslav Yu.

2018-02-01

In present work we studied features of the human brain states classification, corresponding to the real movements of hands and legs. For this purpose we used supervised learning algorithm based on feed-forward artificial neural networks (ANNs) with error back-propagation along with the support vector machine (SVM) method. We compared the quality of operator movements classification by means of EEG signals obtained experimentally in the absence of preliminary processing and after filtration in different ranges up to 25 Hz. It was shown that low-frequency filtering of multichannel EEG data significantly improved accuracy of operator movements classification.

Efficient boundary hunting via vector quantization

NASA Astrophysics Data System (ADS)

Diamantini, Claudia; Panti, Maurizio

2001-03-01

A great amount of information about a classification problem is contained in those instances falling near the decision boundary. This intuition dates back to the earliest studies in pattern recognition, and in the more recent adaptive approaches to the so called boundary hunting, such as the work of Aha et alii on Instance Based Learning and the work of Vapnik et alii on Support Vector Machines. The last work is of particular interest, since theoretical and experimental results ensure the accuracy of boundary reconstruction. However, its optimization approach has heavy computational and memory requirements, which limits its application on huge amounts of data. In the paper we describe an alternative approach to boundary hunting based on adaptive labeled quantization architectures. The adaptation is performed by a stochastic gradient algorithm for the minimization of the error probability. Error probability minimization guarantees the accurate approximation of the optimal decision boundary, while the use of a stochastic gradient algorithm defines an efficient method to reach such approximation. In the paper comparisons to Support Vector Machines are considered.

Color image segmentation with support vector machines: applications to road signs detection.

PubMed

Cyganek, Bogusław

2008-08-01

In this paper we propose efficient color segmentation method which is based on the Support Vector Machine classifier operating in a one-class mode. The method has been developed especially for the road signs recognition system, although it can be used in other applications. The main advantage of the proposed method comes from the fact that the segmentation of characteristic colors is performed not in the original but in the higher dimensional feature space. By this a better data encapsulation with a linear hypersphere can be usually achieved. Moreover, the classifier does not try to capture the whole distribution of the input data which is often difficult to achieve. Instead, the characteristic data samples, called support vectors, are selected which allow construction of the tightest hypersphere that encloses majority of the input data. Then classification of a test data simply consists in a measurement of its distance to a centre of the found hypersphere. The experimental results show high accuracy and speed of the proposed method.

Automated classification of tropical shrub species: a hybrid of leaf shape and machine learning approach

PubMed Central

Murat, Miraemiliana; Abu, Arpah; Yap, Hwa Jen; Yong, Kien-Thai

2017-01-01

Plants play a crucial role in foodstuff, medicine, industry, and environmental protection. The skill of recognising plants is very important in some applications, including conservation of endangered species and rehabilitation of lands after mining activities. However, it is a difficult task to identify plant species because it requires specialized knowledge. Developing an automated classification system for plant species is necessary and valuable since it can help specialists as well as the public in identifying plant species easily. Shape descriptors were applied on the myDAUN dataset that contains 45 tropical shrub species collected from the University of Malaya (UM), Malaysia. Based on literature review, this is the first study in the development of tropical shrub species image dataset and classification using a hybrid of leaf shape and machine learning approach. Four types of shape descriptors were used in this study namely morphological shape descriptors (MSD), Histogram of Oriented Gradients (HOG), Hu invariant moments (Hu) and Zernike moments (ZM). Single descriptor, as well as the combination of hybrid descriptors were tested and compared. The tropical shrub species are classified using six different classifiers, which are artificial neural network (ANN), random forest (RF), support vector machine (SVM), k-nearest neighbour (k-NN), linear discriminant analysis (LDA) and directed acyclic graph multiclass least squares twin support vector machine (DAG MLSTSVM). In addition, three types of feature selection methods were tested in the myDAUN dataset, Relief, Correlation-based feature selection (CFS) and Pearson’s coefficient correlation (PCC). The well-known Flavia dataset and Swedish Leaf dataset were used as the validation dataset on the proposed methods. The results showed that the hybrid of all descriptors of ANN outperformed the other classifiers with an average classification accuracy of 98.23% for the myDAUN dataset, 95.25% for the Flavia dataset and 99.89% for the Swedish Leaf dataset. In addition, the Relief feature selection method achieved the highest classification accuracy of 98.13% after 80 (or 60%) of the original features were reduced, from 133 to 53 descriptors in the myDAUN dataset with the reduction in computational time. Subsequently, the hybridisation of four descriptors gave the best results compared to others. It is proven that the combination MSD and HOG were good enough for tropical shrubs species classification. Hu and ZM descriptors also improved the accuracy in tropical shrubs species classification in terms of invariant to translation, rotation and scale. ANN outperformed the others for tropical shrub species classification in this study. Feature selection methods can be used in the classification of tropical shrub species, as the comparable results could be obtained with the reduced descriptors and reduced in computational time and cost. PMID:28924506

Automated classification of tropical shrub species: a hybrid of leaf shape and machine learning approach.

PubMed

Murat, Miraemiliana; Chang, Siow-Wee; Abu, Arpah; Yap, Hwa Jen; Yong, Kien-Thai

2017-01-01

Plants play a crucial role in foodstuff, medicine, industry, and environmental protection. The skill of recognising plants is very important in some applications, including conservation of endangered species and rehabilitation of lands after mining activities. However, it is a difficult task to identify plant species because it requires specialized knowledge. Developing an automated classification system for plant species is necessary and valuable since it can help specialists as well as the public in identifying plant species easily. Shape descriptors were applied on the myDAUN dataset that contains 45 tropical shrub species collected from the University of Malaya (UM), Malaysia. Based on literature review, this is the first study in the development of tropical shrub species image dataset and classification using a hybrid of leaf shape and machine learning approach. Four types of shape descriptors were used in this study namely morphological shape descriptors (MSD), Histogram of Oriented Gradients (HOG), Hu invariant moments (Hu) and Zernike moments (ZM). Single descriptor, as well as the combination of hybrid descriptors were tested and compared. The tropical shrub species are classified using six different classifiers, which are artificial neural network (ANN), random forest (RF), support vector machine (SVM), k-nearest neighbour (k-NN), linear discriminant analysis (LDA) and directed acyclic graph multiclass least squares twin support vector machine (DAG MLSTSVM). In addition, three types of feature selection methods were tested in the myDAUN dataset, Relief, Correlation-based feature selection (CFS) and Pearson's coefficient correlation (PCC). The well-known Flavia dataset and Swedish Leaf dataset were used as the validation dataset on the proposed methods. The results showed that the hybrid of all descriptors of ANN outperformed the other classifiers with an average classification accuracy of 98.23% for the myDAUN dataset, 95.25% for the Flavia dataset and 99.89% for the Swedish Leaf dataset. In addition, the Relief feature selection method achieved the highest classification accuracy of 98.13% after 80 (or 60%) of the original features were reduced, from 133 to 53 descriptors in the myDAUN dataset with the reduction in computational time. Subsequently, the hybridisation of four descriptors gave the best results compared to others. It is proven that the combination MSD and HOG were good enough for tropical shrubs species classification. Hu and ZM descriptors also improved the accuracy in tropical shrubs species classification in terms of invariant to translation, rotation and scale. ANN outperformed the others for tropical shrub species classification in this study. Feature selection methods can be used in the classification of tropical shrub species, as the comparable results could be obtained with the reduced descriptors and reduced in computational time and cost.

ADMET Evaluation in Drug Discovery. 18. Reliable Prediction of Chemical-Induced Urinary Tract Toxicity by Boosting Machine Learning Approaches.

PubMed

Lei, Tailong; Sun, Huiyong; Kang, Yu; Zhu, Feng; Liu, Hui; Zhou, Wenfang; Wang, Zhe; Li, Dan; Li, Youyong; Hou, Tingjun

2017-11-06

Xenobiotic chemicals and their metabolites are mainly excreted out of our bodies by the urinary tract through the urine. Chemical-induced urinary tract toxicity is one of the main reasons that cause failure during drug development, and it is a common adverse event for medications, natural supplements, and environmental chemicals. Despite its importance, there are only a few in silico models for assessing urinary tract toxicity for a large number of compounds with diverse chemical structures. Here, we developed a series of qualitative and quantitative structure-activity relationship (QSAR) models for predicting urinary tract toxicity. In our study, the recursive feature elimination method incorporated with random forests (RFE-RF) was used for dimension reduction, and then eight machine learning approaches were used for QSAR modeling, i.e., relevance vector machine (RVM), support vector machine (SVM), regularized random forest (RRF), C5.0 trees, eXtreme gradient boosting (XGBoost), AdaBoost.M1, SVM boosting (SVMBoost), and RVM boosting (RVMBoost). For building classification models, the synthetic minority oversampling technique was used to handle the imbalance data set problem. Among all the machine learning approaches, SVMBoost based on the RBF kernel achieves both the best quantitative (q ext 2 = 0.845) and qualitative predictions for the test set (MCC of 0.787, AUC of 0.893, sensitivity of 89.6%, specificity of 94.1%, and global accuracy of 90.8%). The application domains were then analyzed, and all of the tested chemicals fall within the application domain coverage. We also examined the structure features of the chemicals with large prediction errors. In brief, both the regression and classification models developed by the SVMBoost approach have reliable prediction capability for assessing chemical-induced urinary tract toxicity.

Snack food as a modulator of human resting-state functional connectivity.

PubMed

Mendez-Torrijos, Andrea; Kreitz, Silke; Ivan, Claudiu; Konerth, Laura; Rösch, Julie; Pischetsrieder, Monika; Moll, Gunther; Kratz, Oliver; Dörfler, Arnd; Horndasch, Stefanie; Hess, Andreas

2018-04-04

To elucidate the mechanisms of how snack foods may induce non-homeostatic food intake, we used resting state functional magnetic resonance imaging (fMRI), as resting state networks can individually adapt to experience after short time exposures. In addition, we used graph theoretical analysis together with machine learning techniques (support vector machine) to identifying biomarkers that can categorize between high-caloric (potato chips) vs. low-caloric (zucchini) food stimulation. Seventeen healthy human subjects with body mass index (BMI) 19 to 27 underwent 2 different fMRI sessions where an initial resting state scan was acquired, followed by visual presentation of different images of potato chips and zucchini. There was then a 5-minute pause to ingest food (day 1=potato chips, day 3=zucchini), followed by a second resting state scan. fMRI data were further analyzed using graph theory analysis and support vector machine techniques. Potato chips vs. zucchini stimulation led to significant connectivity changes. The support vector machine was able to accurately categorize the 2 types of food stimuli with 100% accuracy. Visual, auditory, and somatosensory structures, as well as thalamus, insula, and basal ganglia were found to be important for food classification. After potato chips consumption, the BMI was associated with the path length and degree in nucleus accumbens, middle temporal gyrus, and thalamus. The results suggest that high vs. low caloric food stimulation in healthy individuals can induce significant changes in resting state networks. These changes can be detected using graph theory measures in conjunction with support vector machine. Additionally, we found that the BMI affects the response of the nucleus accumbens when high caloric food is consumed.

Improving the Accuracy and Training Speed of Motor Imagery Brain-Computer Interfaces Using Wavelet-Based Combined Feature Vectors and Gaussian Mixture Model-Supervectors.

PubMed

Lee, David; Park, Sang-Hoon; Lee, Sang-Goog

2017-10-07

In this paper, we propose a set of wavelet-based combined feature vectors and a Gaussian mixture model (GMM)-supervector to enhance training speed and classification accuracy in motor imagery brain-computer interfaces. The proposed method is configured as follows: first, wavelet transforms are applied to extract the feature vectors for identification of motor imagery electroencephalography (EEG) and principal component analyses are used to reduce the dimensionality of the feature vectors and linearly combine them. Subsequently, the GMM universal background model is trained by the expectation-maximization (EM) algorithm to purify the training data and reduce its size. Finally, a purified and reduced GMM-supervector is used to train the support vector machine classifier. The performance of the proposed method was evaluated for three different motor imagery datasets in terms of accuracy, kappa, mutual information, and computation time, and compared with the state-of-the-art algorithms. The results from the study indicate that the proposed method achieves high accuracy with a small amount of training data compared with the state-of-the-art algorithms in motor imagery EEG classification.

Kernel PLS-SVC for Linear and Nonlinear Discrimination

NASA Technical Reports Server (NTRS)

Rosipal, Roman; Trejo, Leonard J.; Matthews, Bryan

2003-01-01

A new methodology for discrimination is proposed. This is based on kernel orthonormalized partial least squares (PLS) dimensionality reduction of the original data space followed by support vector machines for classification. Close connection of orthonormalized PLS and Fisher's approach to linear discrimination or equivalently with canonical correlation analysis is described. This gives preference to use orthonormalized PLS over principal component analysis. Good behavior of the proposed method is demonstrated on 13 different benchmark data sets and on the real world problem of the classification finger movement periods versus non-movement periods based on electroencephalogram.

Walking pattern analysis and SVM classification based on simulated gaits.

PubMed

Mao, Yuxiang; Saito, Masaru; Kanno, Takehiro; Wei, Daming; Muroi, Hiroyasu

2008-01-01

Three classes of walking patterns, normal, caution and danger, were simulated by tying elastic bands to joints of lower body. In order to distinguish one class from another, four local motions suggested by doctors were investigated stepwise, and differences between levels were evaluated using t-tests. The human adaptability in the tests was also evaluated. We improved average classification accuracy to 84.50% using multiclass support vector machine classifier and concluded that human adaptability is a factor that can cause obvious bias in contiguous data collections.

Deep neural mapping support vector machines.

PubMed

Li, Yujian; Zhang, Ting

2017-09-01

The choice of kernel has an important effect on the performance of a support vector machine (SVM). The effect could be reduced by NEUROSVM, an architecture using multilayer perceptron for feature extraction and SVM for classification. In binary classification, a general linear kernel NEUROSVM can be theoretically simplified as an input layer, many hidden layers, and an SVM output layer. As a feature extractor, the sub-network composed of the input and hidden layers is first trained together with a virtual ordinary output layer by backpropagation, then with the output of its last hidden layer taken as input of the SVM classifier for further training separately. By taking the sub-network as a kernel mapping from the original input space into a feature space, we present a novel model, called deep neural mapping support vector machine (DNMSVM), from the viewpoint of deep learning. This model is also a new and general kernel learning method, where the kernel mapping is indeed an explicit function expressed as a sub-network, different from an implicit function induced by a kernel function traditionally. Moreover, we exploit a two-stage procedure of contrastive divergence learning and gradient descent for DNMSVM to jointly training an adaptive kernel mapping instead of a kernel function, without requirement of kernel tricks. As a whole of the sub-network and the SVM classifier, the joint training of DNMSVM is done by using gradient descent to optimize the objective function with the sub-network layer-wise pre-trained via contrastive divergence learning of restricted Boltzmann machines. Compared to the separate training of NEUROSVM, the joint training is a new algorithm for DNMSVM to have advantages over NEUROSVM. Experimental results show that DNMSVM can outperform NEUROSVM and RBFSVM (i.e., SVM with the kernel of radial basis function), demonstrating its effectiveness. Copyright © 2017 Elsevier Ltd. All rights reserved.

Fast-HPLC Fingerprinting to Discriminate Olive Oil from Other Edible Vegetable Oils by Multivariate Classification Methods.

PubMed

Jiménez-Carvelo, Ana M; González-Casado, Antonio; Pérez-Castaño, Estefanía; Cuadros-Rodríguez, Luis

2017-03-01

A new analytical method for the differentiation of olive oil from other vegetable oils using reversed-phase LC and applying chemometric techniques was developed. A 3 cm short column was used to obtain the chromatographic fingerprint of the methyl-transesterified fraction of each vegetable oil. The chromatographic analysis took only 4 min. The multivariate classification methods used were k-nearest neighbors, partial least-squares (PLS) discriminant analysis, one-class PLS, support vector machine classification, and soft independent modeling of class analogies. The discrimination of olive oil from other vegetable edible oils was evaluated by several classification quality metrics. Several strategies for the classification of the olive oil were used: one input-class, two input-class, and pseudo two input-class.

Support vector machine and fuzzy C-mean clustering-based comparative evaluation of changes in motor cortex electroencephalogram under chronic alcoholism.

PubMed

Kumar, Surendra; Ghosh, Subhojit; Tetarway, Suhash; Sinha, Rakesh Kumar

2015-07-01

In this study, the magnitude and spatial distribution of frequency spectrum in the resting electroencephalogram (EEG) were examined to address the problem of detecting alcoholism in the cerebral motor cortex. The EEG signals were recorded from chronic alcoholic conditions (n = 20) and the control group (n = 20). Data were taken from motor cortex region and divided into five sub-bands (delta, theta, alpha, beta-1 and beta-2). Three methodologies were adopted for feature extraction: (1) absolute power, (2) relative power and (3) peak power frequency. The dimension of the extracted features is reduced by linear discrimination analysis and classified by support vector machine (SVM) and fuzzy C-mean clustering. The maximum classification accuracy (88 %) with SVM clustering was achieved with the EEG spectral features with absolute power frequency on F4 channel. Among the bands, relatively higher classification accuracy was found over theta band and beta-2 band in most of the channels when computed with the EEG features of relative power. Electrodes wise CZ, C3 and P4 were having more alteration. Considering the good classification accuracy obtained by SVM with relative band power features in most of the EEG channels of motor cortex, it can be suggested that the noninvasive automated online diagnostic system for the chronic alcoholic condition can be developed with the help of EEG signals.

Comparisons and Selections of Features and Classifiers for Short Text Classification

NASA Astrophysics Data System (ADS)

Wang, Ye; Zhou, Zhi; Jin, Shan; Liu, Debin; Lu, Mi

2017-10-01

Short text is considerably different from traditional long text documents due to its shortness and conciseness, which somehow hinders the applications of conventional machine learning and data mining algorithms in short text classification. According to traditional artificial intelligence methods, we divide short text classification into three steps, namely preprocessing, feature selection and classifier comparison. In this paper, we have illustrated step-by-step how we approach our goals. Specifically, in feature selection, we compared the performance and robustness of the four methods of one-hot encoding, tf-idf weighting, word2vec and paragraph2vec, and in the classification part, we deliberately chose and compared Naive Bayes, Logistic Regression, Support Vector Machine, K-nearest Neighbor and Decision Tree as our classifiers. Then, we compared and analysed the classifiers horizontally with each other and vertically with feature selections. Regarding the datasets, we crawled more than 400,000 short text files from Shanghai and Shenzhen Stock Exchanges and manually labeled them into two classes, the big and the small. There are eight labels in the big class, and 59 labels in the small class.

Classifying machinery condition using oil samples and binary logistic regression

NASA Astrophysics Data System (ADS)

Phillips, J.; Cripps, E.; Lau, John W.; Hodkiewicz, M. R.

2015-08-01

The era of big data has resulted in an explosion of condition monitoring information. The result is an increasing motivation to automate the costly and time consuming human elements involved in the classification of machine health. When working with industry it is important to build an understanding and hence some trust in the classification scheme for those who use the analysis to initiate maintenance tasks. Typically "black box" approaches such as artificial neural networks (ANN) and support vector machines (SVM) can be difficult to provide ease of interpretability. In contrast, this paper argues that logistic regression offers easy interpretability to industry experts, providing insight to the drivers of the human classification process and to the ramifications of potential misclassification. Of course, accuracy is of foremost importance in any automated classification scheme, so we also provide a comparative study based on predictive performance of logistic regression, ANN and SVM. A real world oil analysis data set from engines on mining trucks is presented and using cross-validation we demonstrate that logistic regression out-performs the ANN and SVM approaches in terms of prediction for healthy/not healthy engines.

Classification of small lesions on dynamic breast MRI: Integrating dimension reduction and out-of-sample extension into CADx methodology

PubMed Central

Nagarajan, Mahesh B.; Huber, Markus B.; Schlossbauer, Thomas; Leinsinger, Gerda; Krol, Andrzej; Wismüller, Axel

2014-01-01

Objective While dimension reduction has been previously explored in computer aided diagnosis (CADx) as an alternative to feature selection, previous implementations of its integration into CADx do not ensure strict separation between training and test data required for the machine learning task. This compromises the integrity of the independent test set, which serves as the basis for evaluating classifier performance. Methods and Materials We propose, implement and evaluate an improved CADx methodology where strict separation is maintained. This is achieved by subjecting the training data alone to dimension reduction; the test data is subsequently processed with out-of-sample extension methods. Our approach is demonstrated in the research context of classifying small diagnostically challenging lesions annotated on dynamic breast magnetic resonance imaging (MRI) studies. The lesions were dynamically characterized through topological feature vectors derived from Minkowski functionals. These feature vectors were then subject to dimension reduction with different linear and non-linear algorithms applied in conjunction with out-of-sample extension techniques. This was followed by classification through supervised learning with support vector regression. Area under the receiver-operating characteristic curve (AUC) was evaluated as the metric of classifier performance. Results Of the feature vectors investigated, the best performance was observed with Minkowski functional ’perimeter’ while comparable performance was observed with ’area’. Of the dimension reduction algorithms tested with ’perimeter’, the best performance was observed with Sammon’s mapping (0.84 ± 0.10) while comparable performance was achieved with exploratory observation machine (0.82 ± 0.09) and principal component analysis (0.80 ± 0.10). Conclusions The results reported in this study with the proposed CADx methodology present a significant improvement over previous results reported with such small lesions on dynamic breast MRI. In particular, non-linear algorithms for dimension reduction exhibited better classification performance than linear approaches, when integrated into our CADx methodology. We also note that while dimension reduction techniques may not necessarily provide an improvement in classification performance over feature selection, they do allow for a higher degree of feature compaction. PMID:24355697

Highly Accurate Classification of Watson-Crick Basepairs on Termini of Single DNA Molecules

PubMed Central

Winters-Hilt, Stephen; Vercoutere, Wenonah; DeGuzman, Veronica S.; Deamer, David; Akeson, Mark; Haussler, David

2003-01-01

We introduce a computational method for classification of individual DNA molecules measured by an α-hemolysin channel detector. We show classification with better than 99% accuracy for DNA hairpin molecules that differ only in their terminal Watson-Crick basepairs. Signal classification was done in silico to establish performance metrics (i.e., where train and test data were of known type, via single-species data files). It was then performed in solution to assay real mixtures of DNA hairpins. Hidden Markov Models (HMMs) were used with Expectation/Maximization for denoising and for associating a feature vector with the ionic current blockade of the DNA molecule. Support Vector Machines (SVMs) were used as discriminators, and were the focus of off-line training. A multiclass SVM architecture was designed to place less discriminatory load on weaker discriminators, and novel SVM kernels were used to boost discrimination strength. The tuning on HMMs and SVMs enabled biophysical analysis of the captured molecule states and state transitions; structure revealed in the biophysical analysis was used for better feature selection. PMID:12547778

Classification of ECG signal with Support Vector Machine Method for Arrhythmia Detection

NASA Astrophysics Data System (ADS)

Turnip, Arjon; Ilham Rizqywan, M.; Kusumandari, Dwi E.; Turnip, Mardi; Sihombing, Poltak

2018-03-01

An electrocardiogram is a potential bioelectric record that occurs as a result of cardiac activity. QRS Detection with zero crossing calculation is one method that can precisely determine peak R of QRS wave as part of arrhythmia detection. In this paper, two experimental scheme (2 minutes duration with different activities: relaxed and, typing) were conducted. From the two experiments it were obtained: accuracy, sensitivity, and positive predictivity about 100% each for the first experiment and about 79%, 93%, 83% for the second experiment, respectively. Furthermore, the feature set of MIT-BIH arrhythmia using the support vector machine (SVM) method on the WEKA software is evaluated. By combining the available attributes on the WEKA algorithm, the result is constant since all classes of SVM goes to the normal class with average 88.49% accuracy.

Comparative decision models for anticipating shortage of food grain production in India

NASA Astrophysics Data System (ADS)

Chattopadhyay, Manojit; Mitra, Subrata Kumar

2018-01-01

This paper attempts to predict food shortages in advance from the analysis of rainfall during the monsoon months along with other inputs used for crop production, such as land used for cereal production, percentage of area covered under irrigation and fertiliser use. We used six binary classification data mining models viz., logistic regression, Multilayer Perceptron, kernel lab-Support Vector Machines, linear discriminant analysis, quadratic discriminant analysis and k-Nearest Neighbors Network, and found that linear discriminant analysis and kernel lab-Support Vector Machines are equally suitable for predicting per capita food shortage with 89.69 % accuracy in overall prediction and 92.06 % accuracy in predicting food shortage ( true negative rate). Advance information of food shortage can help policy makers to take remedial measures in order to prevent devastating consequences arising out of food non-availability.

Quantum optimization for training support vector machines.

PubMed

Anguita, Davide; Ridella, Sandro; Rivieccio, Fabio; Zunino, Rodolfo

2003-01-01

Refined concepts, such as Rademacher estimates of model complexity and nonlinear criteria for weighting empirical classification errors, represent recent and promising approaches to characterize the generalization ability of Support Vector Machines (SVMs). The advantages of those techniques lie in both improving the SVM representation ability and yielding tighter generalization bounds. On the other hand, they often make Quadratic-Programming algorithms no longer applicable, and SVM training cannot benefit from efficient, specialized optimization techniques. The paper considers the application of Quantum Computing to solve the problem of effective SVM training, especially in the case of digital implementations. The presented research compares the behavioral aspects of conventional and enhanced SVMs; experiments in both a synthetic and real-world problems support the theoretical analysis. At the same time, the related differences between Quadratic-Programming and Quantum-based optimization techniques are considered.

Benchmark of Machine Learning Methods for Classification of a SENTINEL-2 Image

NASA Astrophysics Data System (ADS)

Pirotti, F.; Sunar, F.; Piragnolo, M.

2016-06-01

Thanks to mainly ESA and USGS, a large bulk of free images of the Earth is readily available nowadays. One of the main goals of remote sensing is to label images according to a set of semantic categories, i.e. image classification. This is a very challenging issue since land cover of a specific class may present a large spatial and spectral variability and objects may appear at different scales and orientations. In this study, we report the results of benchmarking 9 machine learning algorithms tested for accuracy and speed in training and classification of land-cover classes in a Sentinel-2 dataset. The following machine learning methods (MLM) have been tested: linear discriminant analysis, k-nearest neighbour, random forests, support vector machines, multi layered perceptron, multi layered perceptron ensemble, ctree, boosting, logarithmic regression. The validation is carried out using a control dataset which consists of an independent classification in 11 land-cover classes of an area about 60 km2, obtained by manual visual interpretation of high resolution images (20 cm ground sampling distance) by experts. In this study five out of the eleven classes are used since the others have too few samples (pixels) for testing and validating subsets. The classes used are the following: (i) urban (ii) sowable areas (iii) water (iv) tree plantations (v) grasslands. Validation is carried out using three different approaches: (i) using pixels from the training dataset (train), (ii) using pixels from the training dataset and applying cross-validation with the k-fold method (kfold) and (iii) using all pixels from the control dataset. Five accuracy indices are calculated for the comparison between the values predicted with each model and control values over three sets of data: the training dataset (train), the whole control dataset (full) and with k-fold cross-validation (kfold) with ten folds. Results from validation of predictions of the whole dataset (full) show the random forests method with the highest values; kappa index ranging from 0.55 to 0.42 respectively with the most and least number pixels for training. The two neural networks (multi layered perceptron and its ensemble) and the support vector machines - with default radial basis function kernel - methods follow closely with comparable performance.

A new local-global approach for classification.

PubMed

Peres, R T; Pedreira, C E

2010-09-01

In this paper, we propose a new local-global pattern classification scheme that combines supervised and unsupervised approaches, taking advantage of both, local and global environments. We understand as global methods the ones concerned with the aim of constructing a model for the whole problem space using the totality of the available observations. Local methods focus into sub regions of the space, possibly using an appropriately selected subset of the sample. In the proposed method, the sample is first divided in local cells by using a Vector Quantization unsupervised algorithm, the LBG (Linde-Buzo-Gray). In a second stage, the generated assemblage of much easier problems is locally solved with a scheme inspired by Bayes' rule. Four classification methods were implemented for comparison purposes with the proposed scheme: Learning Vector Quantization (LVQ); Feedforward Neural Networks; Support Vector Machine (SVM) and k-Nearest Neighbors. These four methods and the proposed scheme were implemented in eleven datasets, two controlled experiments, plus nine public available datasets from the UCI repository. The proposed method has shown a quite competitive performance when compared to these classical and largely used classifiers. Our method is simple concerning understanding and implementation and is based on very intuitive concepts. Copyright 2010 Elsevier Ltd. All rights reserved.

A SVM framework for fault detection of the braking system in a high speed train

NASA Astrophysics Data System (ADS)

Liu, Jie; Li, Yan-Fu; Zio, Enrico

2017-03-01

In April 2015, the number of operating High Speed Trains (HSTs) in the world has reached 3603. An efficient, effective and very reliable braking system is evidently very critical for trains running at a speed around 300 km/h. Failure of a highly reliable braking system is a rare event and, consequently, informative recorded data on fault conditions are scarce. This renders the fault detection problem a classification problem with highly unbalanced data. In this paper, a Support Vector Machine (SVM) framework, including feature selection, feature vector selection, model construction and decision boundary optimization, is proposed for tackling this problem. Feature vector selection can largely reduce the data size and, thus, the computational burden. The constructed model is a modified version of the least square SVM, in which a higher cost is assigned to the error of classification of faulty conditions than the error of classification of normal conditions. The proposed framework is successfully validated on a number of public unbalanced datasets. Then, it is applied for the fault detection of braking systems in HST: in comparison with several SVM approaches for unbalanced datasets, the proposed framework gives better results.

Hyperspectral recognition of processing tomato early blight based on GA and SVM

NASA Astrophysics Data System (ADS)

Yin, Xiaojun; Zhao, SiFeng

2013-03-01

Processing tomato early blight seriously affect the yield and quality of its.Determine the leaves spectrum of different disease severity level of processing tomato early blight.We take the sensitive bands of processing tomato early blight as support vector machine input vector.Through the genetic algorithm(GA) to optimize the parameters of SVM, We could recognize different disease severity level of processing tomato early blight.The result show:the sensitive bands of different disease severity levels of processing tomato early blight is 628-643nm and 689-692nm.The sensitive bands are as the GA and SVM input vector.We get the best penalty parameters is 0.129 and kernel function parameters is 3.479.We make classification training and testing by polynomial nuclear,radial basis function nuclear,Sigmoid nuclear.The best classification model is the radial basis function nuclear of SVM. Training accuracy is 84.615%,Testing accuracy is 80.681%.It is combined GA and SVM to achieve multi-classification of processing tomato early blight.It is provided the technical support of prediction processing tomato early blight occurrence, development and diffusion rule in large areas.

Gas Classification Using Deep Convolutional Neural Networks.

PubMed

Peng, Pai; Zhao, Xiaojin; Pan, Xiaofang; Ye, Wenbin

2018-01-08

In this work, we propose a novel Deep Convolutional Neural Network (DCNN) tailored for gas classification. Inspired by the great success of DCNN in the field of computer vision, we designed a DCNN with up to 38 layers. In general, the proposed gas neural network, named GasNet, consists of: six convolutional blocks, each block consist of six layers; a pooling layer; and a fully-connected layer. Together, these various layers make up a powerful deep model for gas classification. Experimental results show that the proposed DCNN method is an effective technique for classifying electronic nose data. We also demonstrate that the DCNN method can provide higher classification accuracy than comparable Support Vector Machine (SVM) methods and Multiple Layer Perceptron (MLP).

Gas Classification Using Deep Convolutional Neural Networks

PubMed Central

Peng, Pai; Zhao, Xiaojin; Pan, Xiaofang; Ye, Wenbin

2018-01-01

In this work, we propose a novel Deep Convolutional Neural Network (DCNN) tailored for gas classification. Inspired by the great success of DCNN in the field of computer vision, we designed a DCNN with up to 38 layers. In general, the proposed gas neural network, named GasNet, consists of: six convolutional blocks, each block consist of six layers; a pooling layer; and a fully-connected layer. Together, these various layers make up a powerful deep model for gas classification. Experimental results show that the proposed DCNN method is an effective technique for classifying electronic nose data. We also demonstrate that the DCNN method can provide higher classification accuracy than comparable Support Vector Machine (SVM) methods and Multiple Layer Perceptron (MLP). PMID:29316723

Hubble Tarantula Treasury Project - VI. Identification of Pre-Main-Sequence Stars using Machine Learning techniques

NASA Astrophysics Data System (ADS)

Ksoll, Victor F.; Gouliermis, Dimitrios A.; Klessen, Ralf S.; Grebel, Eva K.; Sabbi, Elena; Anderson, Jay; Lennon, Daniel J.; Cignoni, Michele; de Marchi, Guido; Smith, Linda J.; Tosi, Monica; van der Marel, Roeland P.

2018-05-01

The Hubble Tarantula Treasury Project (HTTP) has provided an unprecedented photometric coverage of the entire star-burst region of 30 Doradus down to the half Solar mass limit. We use the deep stellar catalogue of HTTP to identify all the pre-main-sequence (PMS) stars of the region, i.e., stars that have not started their lives on the main-sequence yet. The photometric distinction of these stars from the more evolved populations is not a trivial task due to several factors that alter their colour-magnitude diagram positions. The identification of PMS stars requires, thus, sophisticated statistical methods. We employ Machine Learning Classification techniques on the HTTP survey of more than 800,000 sources to identify the PMS stellar content of the observed field. Our methodology consists of 1) carefully selecting the most probable low-mass PMS stellar population of the star-forming cluster NGC2070, 2) using this sample to train classification algorithms to build a predictive model for PMS stars, and 3) applying this model in order to identify the most probable PMS content across the entire Tarantula Nebula. We employ Decision Tree, Random Forest and Support Vector Machine classifiers to categorise the stars as PMS and Non-PMS. The Random Forest and Support Vector Machine provided the most accurate models, predicting about 20,000 sources with a candidateship probability higher than 50 percent, and almost 10,000 PMS candidates with a probability higher than 95 percent. This is the richest and most accurate photometric catalogue of extragalactic PMS candidates across the extent of a whole star-forming complex.

Mining protein function from text using term-based support vector machines

PubMed Central

Rice, Simon B; Nenadic, Goran; Stapley, Benjamin J

2005-01-01

Background Text mining has spurred huge interest in the domain of biology. The goal of the BioCreAtIvE exercise was to evaluate the performance of current text mining systems. We participated in Task 2, which addressed assigning Gene Ontology terms to human proteins and selecting relevant evidence from full-text documents. We approached it as a modified form of the document classification task. We used a supervised machine-learning approach (based on support vector machines) to assign protein function and select passages that support the assignments. As classification features, we used a protein's co-occurring terms that were automatically extracted from documents. Results The results evaluated by curators were modest, and quite variable for different problems: in many cases we have relatively good assignment of GO terms to proteins, but the selected supporting text was typically non-relevant (precision spanning from 3% to 50%). The method appears to work best when a substantial set of relevant documents is obtained, while it works poorly on single documents and/or short passages. The initial results suggest that our approach can also mine annotations from text even when an explicit statement relating a protein to a GO term is absent. Conclusion A machine learning approach to mining protein function predictions from text can yield good performance only if sufficient training data is available, and significant amount of supporting data is used for prediction. The most promising results are for combined document retrieval and GO term assignment, which calls for the integration of methods developed in BioCreAtIvE Task 1 and Task 2. PMID:15960835

Prediction and Identification of Krüppel-Like Transcription Factors by Machine Learning Method.

PubMed

Liao, Zhijun; Wang, Xinrui; Chen, Xingyong; Zou, Quan

2017-01-01

The Krüppel-like factors (KLFs) are a family of containing Zn finger(ZF) motif transcription factors with 18 members in human genome, among them, KLF18 is predicted by bioinformatics. KLFs possess various physiological function involving in a number of cancers and other diseases. Here we perform a binary-class classification of KLFs and non-KLFs by machine learning methods. The protein sequences of KLFs and non-KLFs were searched from UniProt and randomly separate them into training dataset(containing positive and negative sequences) and test dataset(containing only negative sequences), after extracting the 188-dimensional(188D) feature vectors we carry out category with four classifiers(GBDT, libSVM, RF, and k-NN). On the human KLFs, we further dig into the evolutionary relationship and motif distribution, and finally we analyze the conserved amino acid residue of three zinc fingers. The classifier model from training dataset were well constructed, and the highest specificity(Sp) was 99.83% from a library for support vector machine(libSVM) and all the correctly classified rates were over 70% for 10-fold cross-validation on test dataset. The 18 human KLFs can be further divided into 7 groups and the zinc finger domains were located at the carboxyl terminus, and many conserved amino acid residues including Cysteine and Histidine, and the span and interval between them were consistent in the three ZF domains. Two classification models for KLFs prediction have been built by novel machine learning methods. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

Classification of toxicity effects of biotransformed hepatic drugs using whale optimized support vector machines.

PubMed

Tharwat, Alaa; Moemen, Yasmine S; Hassanien, Aboul Ella

2017-04-01

Measuring toxicity is an important step in drug development. Nevertheless, the current experimental methods used to estimate the drug toxicity are expensive and time-consuming, indicating that they are not suitable for large-scale evaluation of drug toxicity in the early stage of drug development. Hence, there is a high demand to develop computational models that can predict the drug toxicity risks. In this study, we used a dataset that consists of 553 drugs that biotransformed in liver. The toxic effects were calculated for the current data, namely, mutagenic, tumorigenic, irritant and reproductive effect. Each drug is represented by 31 chemical descriptors (features). The proposed model consists of three phases. In the first phase, the most discriminative subset of features is selected using rough set-based methods to reduce the classification time while improving the classification performance. In the second phase, different sampling methods such as Random Under-Sampling, Random Over-Sampling and Synthetic Minority Oversampling Technique (SMOTE), BorderLine SMOTE and Safe Level SMOTE are used to solve the problem of imbalanced dataset. In the third phase, the Support Vector Machines (SVM) classifier is used to classify an unknown drug into toxic or non-toxic. SVM parameters such as the penalty parameter and kernel parameter have a great impact on the classification accuracy of the model. In this paper, Whale Optimization Algorithm (WOA) has been proposed to optimize the parameters of SVM, so that the classification error can be reduced. The experimental results proved that the proposed model achieved high sensitivity to all toxic effects. Overall, the high sensitivity of the WOA+SVM model indicates that it could be used for the prediction of drug toxicity in the early stage of drug development. Copyright © 2017 Elsevier Inc. All rights reserved.

Predicting surface fuel models and fuel metrics using lidar and CIR imagery in a dense mixed conifer forest

Treesearch

Marek K. Jakubowksi; Qinghua Guo; Brandon Collins; Scott Stephens; Maggi Kelly

2013-01-01

We compared the ability of several classification and regression algorithms to predict forest stand structure metrics and standard surface fuel models. Our study area spans a dense, topographically complex Sierra Nevada mixed-conifer forest. We used clustering, regression trees, and support vector machine algorithms to analyze high density (average 9 pulses/m

Prediction of cause of death from forensic autopsy reports using text classification techniques: A comparative study.

PubMed

Mujtaba, Ghulam; Shuib, Liyana; Raj, Ram Gopal; Rajandram, Retnagowri; Shaikh, Khairunisa

2018-07-01

Automatic text classification techniques are useful for classifying plaintext medical documents. This study aims to automatically predict the cause of death from free text forensic autopsy reports by comparing various schemes for feature extraction, term weighing or feature value representation, text classification, and feature reduction. For experiments, the autopsy reports belonging to eight different causes of death were collected, preprocessed and converted into 43 master feature vectors using various schemes for feature extraction, representation, and reduction. The six different text classification techniques were applied on these 43 master feature vectors to construct a classification model that can predict the cause of death. Finally, classification model performance was evaluated using four performance measures i.e. overall accuracy, macro precision, macro-F-measure, and macro recall. From experiments, it was found that that unigram features obtained the highest performance compared to bigram, trigram, and hybrid-gram features. Furthermore, in feature representation schemes, term frequency, and term frequency with inverse document frequency obtained similar and better results when compared with binary frequency, and normalized term frequency with inverse document frequency. Furthermore, the chi-square feature reduction approach outperformed Pearson correlation, and information gain approaches. Finally, in text classification algorithms, support vector machine classifier outperforms random forest, Naive Bayes, k-nearest neighbor, decision tree, and ensemble-voted classifier. Our results and comparisons hold practical importance and serve as references for future works. Moreover, the comparison outputs will act as state-of-art techniques to compare future proposals with existing automated text classification techniques. Copyright © 2017 Elsevier Ltd and Faculty of Forensic and Legal Medicine. All rights reserved.

Vehicle Classification Using an Imbalanced Dataset Based on a Single Magnetic Sensor.

PubMed

Xu, Chang; Wang, Yingguan; Bao, Xinghe; Li, Fengrong

2018-05-24

This paper aims to improve the accuracy of automatic vehicle classifiers for imbalanced datasets. Classification is made through utilizing a single anisotropic magnetoresistive sensor, with the models of vehicles involved being classified into hatchbacks, sedans, buses, and multi-purpose vehicles (MPVs). Using time domain and frequency domain features in combination with three common classification algorithms in pattern recognition, we develop a novel feature extraction method for vehicle classification. These three common classification algorithms are the k-nearest neighbor, the support vector machine, and the back-propagation neural network. Nevertheless, a problem remains with the original vehicle magnetic dataset collected being imbalanced, and may lead to inaccurate classification results. With this in mind, we propose an approach called SMOTE, which can further boost the performance of classifiers. Experimental results show that the k-nearest neighbor (KNN) classifier with the SMOTE algorithm can reach a classification accuracy of 95.46%, thus minimizing the effect of the imbalance.

Comparison of Feature Selection Techniques in Machine Learning for Anatomical Brain MRI in Dementia.

PubMed

Tohka, Jussi; Moradi, Elaheh; Huttunen, Heikki

2016-07-01

We present a comparative split-half resampling analysis of various data driven feature selection and classification methods for the whole brain voxel-based classification analysis of anatomical magnetic resonance images. We compared support vector machines (SVMs), with or without filter based feature selection, several embedded feature selection methods and stability selection. While comparisons of the accuracy of various classification methods have been reported previously, the variability of the out-of-training sample classification accuracy and the set of selected features due to independent training and test sets have not been previously addressed in a brain imaging context. We studied two classification problems: 1) Alzheimer's disease (AD) vs. normal control (NC) and 2) mild cognitive impairment (MCI) vs. NC classification. In AD vs. NC classification, the variability in the test accuracy due to the subject sample did not vary between different methods and exceeded the variability due to different classifiers. In MCI vs. NC classification, particularly with a large training set, embedded feature selection methods outperformed SVM-based ones with the difference in the test accuracy exceeding the test accuracy variability due to the subject sample. The filter and embedded methods produced divergent feature patterns for MCI vs. NC classification that suggests the utility of the embedded feature selection for this problem when linked with the good generalization performance. The stability of the feature sets was strongly correlated with the number of features selected, weakly correlated with the stability of classification accuracy, and uncorrelated with the average classification accuracy.

Comparison of Random Forest and Support Vector Machine classifiers using UAV remote sensing imagery

NASA Astrophysics Data System (ADS)

Piragnolo, Marco; Masiero, Andrea; Pirotti, Francesco

2017-04-01

Since recent years surveying with unmanned aerial vehicles (UAV) is getting a great amount of attention due to decreasing costs, higher precision and flexibility of usage. UAVs have been applied for geomorphological investigations, forestry, precision agriculture, cultural heritage assessment and for archaeological purposes. It can be used for land use and land cover classification (LULC). In literature, there are two main types of approaches for classification of remote sensing imagery: pixel-based and object-based. On one hand, pixel-based approach mostly uses training areas to define classes and respective spectral signatures. On the other hand, object-based classification considers pixels, scale, spatial information and texture information for creating homogeneous objects. Machine learning methods have been applied successfully for classification, and their use is increasing due to the availability of faster computing capabilities. The methods learn and train the model from previous computation. Two machine learning methods which have given good results in previous investigations are Random Forest (RF) and Support Vector Machine (SVM). The goal of this work is to compare RF and SVM methods for classifying LULC using images collected with a fixed wing UAV. The processing chain regarding classification uses packages in R, an open source scripting language for data analysis, which provides all necessary algorithms. The imagery was acquired and processed in November 2015 with cameras providing information over the red, blue, green and near infrared wavelength reflectivity over a testing area in the campus of Agripolis, in Italy. Images were elaborated and ortho-rectified through Agisoft Photoscan. The ortho-rectified image is the full data set, and the test set is derived from partial sub-setting of the full data set. Different tests have been carried out, using a percentage from 2 % to 20 % of the total. Ten training sets and ten validation sets are obtained from each test set. The control dataset consist of an independent visual classification done by an expert over the whole area. The classes are (i) broadleaf, (ii) building, (iii) grass, (iv) headland access path, (v) road, (vi) sowed land, (vii) vegetable. The RF and SVM are applied to the test set. The performances of the methods are evaluated using the three following accuracy metrics: Kappa index, Classification accuracy and Classification Error. All three are calculated in three different ways: with K-fold cross validation, using the validation test set and using the full test set. The analysis indicates that SVM gets better results in terms of good scores using K-fold cross or validation test set. Using the full test set, RF achieves a better result in comparison to SVM. It also seems that SVM performs better with smaller training sets, whereas RF performs better as training sets get larger.

The assisted prediction modelling frame with hybridisation and ensemble for business risk forecasting and an implementation

NASA Astrophysics Data System (ADS)

Li, Hui; Hong, Lu-Yao; Zhou, Qing; Yu, Hai-Jie

2015-08-01

The business failure of numerous companies results in financial crises. The high social costs associated with such crises have made people to search for effective tools for business risk prediction, among which, support vector machine is very effective. Several modelling means, including single-technique modelling, hybrid modelling, and ensemble modelling, have been suggested in forecasting business risk with support vector machine. However, existing literature seldom focuses on the general modelling frame for business risk prediction, and seldom investigates performance differences among different modelling means. We reviewed researches on forecasting business risk with support vector machine, proposed the general assisted prediction modelling frame with hybridisation and ensemble (APMF-WHAE), and finally, investigated the use of principal components analysis, support vector machine, random sampling, and group decision, under the general frame in forecasting business risk. Under the APMF-WHAE frame with support vector machine as the base predictive model, four specific predictive models were produced, namely, pure support vector machine, a hybrid support vector machine involved with principal components analysis, a support vector machine ensemble involved with random sampling and group decision, and an ensemble of hybrid support vector machine using group decision to integrate various hybrid support vector machines on variables produced from principle components analysis and samples from random sampling. The experimental results indicate that hybrid support vector machine and ensemble of hybrid support vector machines were able to produce dominating performance than pure support vector machine and support vector machine ensemble.

Deep Learning Accurately Predicts Estrogen Receptor Status in Breast Cancer Metabolomics Data.

PubMed

Alakwaa, Fadhl M; Chaudhary, Kumardeep; Garmire, Lana X

2018-01-05

Metabolomics holds the promise as a new technology to diagnose highly heterogeneous diseases. Conventionally, metabolomics data analysis for diagnosis is done using various statistical and machine learning based classification methods. However, it remains unknown if deep neural network, a class of increasingly popular machine learning methods, is suitable to classify metabolomics data. Here we use a cohort of 271 breast cancer tissues, 204 positive estrogen receptor (ER+), and 67 negative estrogen receptor (ER-) to test the accuracies of feed-forward networks, a deep learning (DL) framework, as well as six widely used machine learning models, namely random forest (RF), support vector machines (SVM), recursive partitioning and regression trees (RPART), linear discriminant analysis (LDA), prediction analysis for microarrays (PAM), and generalized boosted models (GBM). DL framework has the highest area under the curve (AUC) of 0.93 in classifying ER+/ER- patients, compared to the other six machine learning algorithms. Furthermore, the biological interpretation of the first hidden layer reveals eight commonly enriched significant metabolomics pathways (adjusted P-value <0.05) that cannot be discovered by other machine learning methods. Among them, protein digestion and absorption and ATP-binding cassette (ABC) transporters pathways are also confirmed in integrated analysis between metabolomics and gene expression data in these samples. In summary, deep learning method shows advantages for metabolomics based breast cancer ER status classification, with both the highest prediction accuracy (AUC = 0.93) and better revelation of disease biology. We encourage the adoption of feed-forward networks based deep learning method in the metabolomics research community for classification.

An empirical comparison of different approaches for combining multimodal neuroimaging data with support vector machine

PubMed Central

Pettersson-Yeo, William; Benetti, Stefania; Marquand, Andre F.; Joules, Richard; Catani, Marco; Williams, Steve C. R.; Allen, Paul; McGuire, Philip; Mechelli, Andrea

2014-01-01

In the pursuit of clinical utility, neuroimaging researchers of psychiatric and neurological illness are increasingly using analyses, such as support vector machine, that allow inference at the single-subject level. Recent studies employing single-modality data, however, suggest that classification accuracies must be improved for such utility to be realized. One possible solution is to integrate different data types to provide a single combined output classification; either by generating a single decision function based on an integrated kernel matrix, or, by creating an ensemble of multiple single modality classifiers and integrating their predictions. Here, we describe four integrative approaches: (1) an un-weighted sum of kernels, (2) multi-kernel learning, (3) prediction averaging, and (4) majority voting, and compare their ability to enhance classification accuracy relative to the best single-modality classification accuracy. We achieve this by integrating structural, functional, and diffusion tensor magnetic resonance imaging data, in order to compare ultra-high risk (n = 19), first episode psychosis (n = 19) and healthy control subjects (n = 23). Our results show that (i) whilst integration can enhance classification accuracy by up to 13%, the frequency of such instances may be limited, (ii) where classification can be enhanced, simple methods may yield greater increases relative to more computationally complex alternatives, and, (iii) the potential for classification enhancement is highly influenced by the specific diagnostic comparison under consideration. In conclusion, our findings suggest that for moderately sized clinical neuroimaging datasets, combining different imaging modalities in a data-driven manner is no “magic bullet” for increasing classification accuracy. However, it remains possible that this conclusion is dependent on the use of neuroimaging modalities that had little, or no, complementary information to offer one another, and that the integration of more diverse types of data would have produced greater classification enhancement. We suggest that future studies ideally examine a greater variety of data types (e.g., genetic, cognitive, and neuroimaging) in order to identify the data types and combinations optimally suited to the classification of early stage psychosis. PMID:25076868

An empirical comparison of different approaches for combining multimodal neuroimaging data with support vector machine.

PubMed

Pettersson-Yeo, William; Benetti, Stefania; Marquand, Andre F; Joules, Richard; Catani, Marco; Williams, Steve C R; Allen, Paul; McGuire, Philip; Mechelli, Andrea

2014-01-01

In the pursuit of clinical utility, neuroimaging researchers of psychiatric and neurological illness are increasingly using analyses, such as support vector machine, that allow inference at the single-subject level. Recent studies employing single-modality data, however, suggest that classification accuracies must be improved for such utility to be realized. One possible solution is to integrate different data types to provide a single combined output classification; either by generating a single decision function based on an integrated kernel matrix, or, by creating an ensemble of multiple single modality classifiers and integrating their predictions. Here, we describe four integrative approaches: (1) an un-weighted sum of kernels, (2) multi-kernel learning, (3) prediction averaging, and (4) majority voting, and compare their ability to enhance classification accuracy relative to the best single-modality classification accuracy. We achieve this by integrating structural, functional, and diffusion tensor magnetic resonance imaging data, in order to compare ultra-high risk (n = 19), first episode psychosis (n = 19) and healthy control subjects (n = 23). Our results show that (i) whilst integration can enhance classification accuracy by up to 13%, the frequency of such instances may be limited, (ii) where classification can be enhanced, simple methods may yield greater increases relative to more computationally complex alternatives, and, (iii) the potential for classification enhancement is highly influenced by the specific diagnostic comparison under consideration. In conclusion, our findings suggest that for moderately sized clinical neuroimaging datasets, combining different imaging modalities in a data-driven manner is no "magic bullet" for increasing classification accuracy. However, it remains possible that this conclusion is dependent on the use of neuroimaging modalities that had little, or no, complementary information to offer one another, and that the integration of more diverse types of data would have produced greater classification enhancement. We suggest that future studies ideally examine a greater variety of data types (e.g., genetic, cognitive, and neuroimaging) in order to identify the data types and combinations optimally suited to the classification of early stage psychosis.

A support vector machine-based method to identify mild cognitive impairment with multi-level characteristics of magnetic resonance imaging.

PubMed

Long, Zhuqing; Jing, Bin; Yan, Huagang; Dong, Jianxin; Liu, Han; Mo, Xiao; Han, Ying; Li, Haiyun

2016-09-07

Mild cognitive impairment (MCI) represents a transitional state between normal aging and Alzheimer's disease (AD). Non-invasive diagnostic methods are desirable to identify MCI for early therapeutic interventions. In this study, we proposed a support vector machine (SVM)-based method to discriminate between MCI patients and normal controls (NCs) using multi-level characteristics of magnetic resonance imaging (MRI). This method adopted a radial basis function (RBF) as the kernel function, and a grid search method to optimize the two parameters of SVM. The calculated characteristics, i.e., the Hurst exponent (HE), amplitude of low-frequency fluctuations (ALFF), regional homogeneity (ReHo) and gray matter density (GMD), were adopted as the classification features. A leave-one-out cross-validation (LOOCV) was used to evaluate the classification performance of the method. Applying the proposed method to the experimental data from 29 MCI patients and 33 healthy subjects, we achieved a classification accuracy of up to 96.77%, with a sensitivity of 93.10% and a specificity of 100%, and the area under the curve (AUC) yielded up to 0.97. Furthermore, the most discriminative features for classification were found to predominantly involve default-mode regions, such as hippocampus (HIP), parahippocampal gyrus (PHG), posterior cingulate gyrus (PCG) and middle frontal gyrus (MFG), and subcortical regions such as lentiform nucleus (LN) and amygdala (AMYG). Therefore, our method is promising in distinguishing MCI patients from NCs and may be useful for the diagnosis of MCI. Copyright © 2016 IBRO. Published by Elsevier Ltd. All rights reserved.

Differentiation of several interstitial lung disease patterns in HRCT images using support vector machine: role of databases on performance

NASA Astrophysics Data System (ADS)

Kale, Mandar; Mukhopadhyay, Sudipta; Dash, Jatindra K.; Garg, Mandeep; Khandelwal, Niranjan

2016-03-01

Interstitial lung disease (ILD) is complicated group of pulmonary disorders. High Resolution Computed Tomography (HRCT) considered to be best imaging technique for analysis of different pulmonary disorders. HRCT findings can be categorised in several patterns viz. Consolidation, Emphysema, Ground Glass Opacity, Nodular, Normal etc. based on their texture like appearance. Clinician often find it difficult to diagnosis these pattern because of their complex nature. In such scenario computer-aided diagnosis system could help clinician to identify patterns. Several approaches had been proposed for classification of ILD patterns. This includes computation of textural feature and training /testing of classifier such as artificial neural network (ANN), support vector machine (SVM) etc. In this paper, wavelet features are calculated from two different ILD database, publically available MedGIFT ILD database and private ILD database, followed by performance evaluation of ANN and SVM classifiers in terms of average accuracy. It is found that average classification accuracy by SVM is greater than ANN where trained and tested on same database. Investigation continued further to test variation in accuracy of classifier when training and testing is performed with alternate database and training and testing of classifier with database formed by merging samples from same class from two individual databases. The average classification accuracy drops when two independent databases used for training and testing respectively. There is significant improvement in average accuracy when classifiers are trained and tested with merged database. It infers dependency of classification accuracy on training data. It is observed that SVM outperforms ANN when same database is used for training and testing.

Natural Language Processing Based Instrument for Classification of Free Text Medical Records

PubMed Central

2016-01-01

According to the Ministry of Labor, Health and Social Affairs of Georgia a new health management system has to be introduced in the nearest future. In this context arises the problem of structuring and classifying documents containing all the history of medical services provided. The present work introduces the instrument for classification of medical records based on the Georgian language. It is the first attempt of such classification of the Georgian language based medical records. On the whole 24.855 examination records have been studied. The documents were classified into three main groups (ultrasonography, endoscopy, and X-ray) and 13 subgroups using two well-known methods: Support Vector Machine (SVM) and K-Nearest Neighbor (KNN). The results obtained demonstrated that both machine learning methods performed successfully, with a little supremacy of SVM. In the process of classification a “shrink” method, based on features selection, was introduced and applied. At the first stage of classification the results of the “shrink” case were better; however, on the second stage of classification into subclasses 23% of all documents could not be linked to only one definite individual subclass (liver or binary system) due to common features characterizing these subclasses. The overall results of the study were successful. PMID:27668260

A machine learning framework involving EEG-based functional connectivity to diagnose major depressive disorder (MDD).

PubMed

Mumtaz, Wajid; Ali, Syed Saad Azhar; Yasin, Mohd Azhar Mohd; Malik, Aamir Saeed

2018-02-01

Major depressive disorder (MDD), a debilitating mental illness, could cause functional disabilities and could become a social problem. An accurate and early diagnosis for depression could become challenging. This paper proposed a machine learning framework involving EEG-derived synchronization likelihood (SL) features as input data for automatic diagnosis of MDD. It was hypothesized that EEG-based SL features could discriminate MDD patients and healthy controls with an acceptable accuracy better than measures such as interhemispheric coherence and mutual information. In this work, classification models such as support vector machine (SVM), logistic regression (LR) and Naïve Bayesian (NB) were employed to model relationship between the EEG features and the study groups (MDD patient and healthy controls) and ultimately achieved discrimination of study participants. The results indicated that the classification rates were better than chance. More specifically, the study resulted into SVM classification accuracy = 98%, sensitivity = 99.9%, specificity = 95% and f-measure = 0.97; LR classification accuracy = 91.7%, sensitivity = 86.66%, specificity = 96.6% and f-measure = 0.90; NB classification accuracy = 93.6%, sensitivity = 100%, specificity = 87.9% and f-measure = 0.95. In conclusion, SL could be a promising method for diagnosing depression. The findings could be generalized to develop a robust CAD-based tool that may help for clinical purposes.

Engine classification using vibrations measured by Laser Doppler Vibrometer on different surfaces

NASA Astrophysics Data System (ADS)

Wei, J.; Liu, Chi-Him; Zhu, Zhigang; Vongsy, Karmon; Mendoza-Schrock, Olga

2015-05-01

In our previous studies, vehicle surfaces' vibrations caused by operating engines measured by Laser Doppler Vibrometer (LDV) have been effectively exploited in order to classify vehicles of different types, e.g., vans, 2-door sedans, 4-door sedans, trucks, and buses, as well as different types of engines, such as Inline-four engines, V-6 engines, 1-axle diesel engines, and 2-axle diesel engines. The results are achieved by employing methods based on an array of machine learning classifiers such as AdaBoost, random forests, neural network, and support vector machines. To achieve effective classification performance, we seek to find a more reliable approach to pick authentic vibrations of vehicle engines from a trustworthy surface. Compared with vibrations directly taken from the uncooperative vehicle surfaces that are rigidly connected to the engines, these vibrations are much weaker in magnitudes. In this work we conducted a systematic study on different types of objects. We tested different types of engines ranging from electric shavers, electric fans, and coffee machines among different surfaces such as a white board, cement wall, and steel case to investigate the characteristics of the LDV signals of these surfaces, in both the time and spectral domains. Preliminary results in engine classification using several machine learning algorithms point to the right direction on the choice of type of object surfaces to be planted for LDV measurements.

Model-based and Model-free Machine Learning Techniques for Diagnostic Prediction and Classification of Clinical Outcomes in Parkinson's Disease.

PubMed

Gao, Chao; Sun, Hanbo; Wang, Tuo; Tang, Ming; Bohnen, Nicolaas I; Müller, Martijn L T M; Herman, Talia; Giladi, Nir; Kalinin, Alexandr; Spino, Cathie; Dauer, William; Hausdorff, Jeffrey M; Dinov, Ivo D

2018-05-08

In this study, we apply a multidisciplinary approach to investigate falls in PD patients using clinical, demographic and neuroimaging data from two independent initiatives (University of Michigan and Tel Aviv Sourasky Medical Center). Using machine learning techniques, we construct predictive models to discriminate fallers and non-fallers. Through controlled feature selection, we identified the most salient predictors of patient falls including gait speed, Hoehn and Yahr stage, postural instability and gait difficulty-related measurements. The model-based and model-free analytical methods we employed included logistic regression, random forests, support vector machines, and XGboost. The reliability of the forecasts was assessed by internal statistical (5-fold) cross validation as well as by external out-of-bag validation. Four specific challenges were addressed in the study: Challenge 1, develop a protocol for harmonizing and aggregating complex, multisource, and multi-site Parkinson's disease data; Challenge 2, identify salient predictive features associated with specific clinical traits, e.g., patient falls; Challenge 3, forecast patient falls and evaluate the classification performance; and Challenge 4, predict tremor dominance (TD) vs. posture instability and gait difficulty (PIGD). Our findings suggest that, compared to other approaches, model-free machine learning based techniques provide a more reliable clinical outcome forecasting of falls in Parkinson's patients, for example, with a classification accuracy of about 70-80%.

Spatial Mutual Information Based Hyperspectral Band Selection for Classification

PubMed Central

2015-01-01

The amount of information involved in hyperspectral imaging is large. Hyperspectral band selection is a popular method for reducing dimensionality. Several information based measures such as mutual information have been proposed to reduce information redundancy among spectral bands. Unfortunately, mutual information does not take into account the spatial dependency between adjacent pixels in images thus reducing its robustness as a similarity measure. In this paper, we propose a new band selection method based on spatial mutual information. As validation criteria, a supervised classification method using support vector machine (SVM) is used. Experimental results of the classification of hyperspectral datasets show that the proposed method can achieve more accurate results. PMID:25918742

Nonlinear, non-stationary image processing technique for eddy current NDE

NASA Astrophysics Data System (ADS)

Yang, Guang; Dib, Gerges; Kim, Jaejoon; Zhang, Lu; Xin, Junjun; Udpa, Lalita

2012-05-01

Automatic analysis of eddy current (EC) data has facilitated the analysis of large volumes of data generated in the inspection of steam generator tubes in nuclear power plants. The traditional procedure for analysis of EC data includes data calibration, pre-processing, region of interest (ROI) detection, feature extraction and classification. Accurate ROI detection has been enhanced by pre-processing, which involves reducing noise and other undesirable components as well as enhancing defect indications in the raw measurement. This paper presents the Hilbert-Huang Transform (HHT) for feature extraction and support vector machine (SVM) for classification. The performance is shown to significantly better than the existing rule based classification approach used in industry.

Resident Space Object Characterization and Behavior Understanding via Machine Learning and Ontology-based Bayesian Networks

NASA Astrophysics Data System (ADS)

Furfaro, R.; Linares, R.; Gaylor, D.; Jah, M.; Walls, R.

2016-09-01

In this paper, we present an end-to-end approach that employs machine learning techniques and Ontology-based Bayesian Networks (BN) to characterize the behavior of resident space objects. State-of-the-Art machine learning architectures (e.g. Extreme Learning Machines, Convolutional Deep Networks) are trained on physical models to learn the Resident Space Object (RSO) features in the vectorized energy and momentum states and parameters. The mapping from measurements to vectorized energy and momentum states and parameters enables behavior characterization via clustering in the features space and subsequent RSO classification. Additionally, Space Object Behavioral Ontologies (SOBO) are employed to define and capture the domain knowledge-base (KB) and BNs are constructed from the SOBO in a semi-automatic fashion to execute probabilistic reasoning over conclusions drawn from trained classifiers and/or directly from processed data. Such an approach enables integrating machine learning classifiers and probabilistic reasoning to support higher-level decision making for space domain awareness applications. The innovation here is to use these methods (which have enjoyed great success in other domains) in synergy so that it enables a "from data to discovery" paradigm by facilitating the linkage and fusion of large and disparate sources of information via a Big Data Science and Analytics framework.

Biomarkers of Eating Disorders Using Support Vector Machine Analysis of Structural Neuroimaging Data: Preliminary Results

PubMed Central

Cerasa, Antonio; Castiglioni, Isabella; Salvatore, Christian; Funaro, Angela; Martino, Iolanda; Alfano, Stefania; Donzuso, Giulia; Perrotta, Paolo; Gioia, Maria Cecilia; Gilardi, Maria Carla; Quattrone, Aldo

2015-01-01

Presently, there are no valid biomarkers to identify individuals with eating disorders (ED). The aim of this work was to assess the feasibility of a machine learning method for extracting reliable neuroimaging features allowing individual categorization of patients with ED. Support Vector Machine (SVM) technique, combined with a pattern recognition method, was employed utilizing structural magnetic resonance images. Seventeen females with ED (six with diagnosis of anorexia nervosa and 11 with bulimia nervosa) were compared against 17 body mass index-matched healthy controls (HC). Machine learning allowed individual diagnosis of ED versus HC with an Accuracy ≥ 0.80. Voxel-based pattern recognition analysis demonstrated that voxels influencing the classification Accuracy involved the occipital cortex, the posterior cerebellar lobule, precuneus, sensorimotor/premotor cortices, and the medial prefrontal cortex, all critical regions known to be strongly involved in the pathophysiological mechanisms of ED. Although these findings should be considered preliminary given the small size investigated, SVM analysis highlights the role of well-known brain regions as possible biomarkers to distinguish ED from HC at an individual level, thus encouraging the translational implementation of this new multivariate approach in the clinical practice. PMID:26648660

Support vector machine for breast cancer classification using diffusion-weighted MRI histogram features: Preliminary study.

PubMed

Vidić, Igor; Egnell, Liv; Jerome, Neil P; Teruel, Jose R; Sjøbakk, Torill E; Østlie, Agnes; Fjøsne, Hans E; Bathen, Tone F; Goa, Pål Erik

2018-05-01

Diffusion-weighted MRI (DWI) is currently one of the fastest developing MRI-based techniques in oncology. Histogram properties from model fitting of DWI are useful features for differentiation of lesions, and classification can potentially be improved by machine learning. To evaluate classification of malignant and benign tumors and breast cancer subtypes using support vector machine (SVM). Prospective. Fifty-one patients with benign (n = 23) and malignant (n = 28) breast tumors (26 ER+, whereof six were HER2+). Patients were imaged with DW-MRI (3T) using twice refocused spin-echo echo-planar imaging with echo time / repetition time (TR/TE) = 9000/86 msec, 90 × 90 matrix size, 2 × 2 mm in-plane resolution, 2.5 mm slice thickness, and 13 b-values. Apparent diffusion coefficient (ADC), relative enhanced diffusivity (RED), and the intravoxel incoherent motion (IVIM) parameters diffusivity (D), pseudo-diffusivity (D*), and perfusion fraction (f) were calculated. The histogram properties (median, mean, standard deviation, skewness, kurtosis) were used as features in SVM (10-fold cross-validation) for differentiation of lesions and subtyping. Accuracies of the SVM classifications were calculated to find the combination of features with highest prediction accuracy. Mann-Whitney tests were performed for univariate comparisons. For benign versus malignant tumors, univariate analysis found 11 histogram properties to be significant differentiators. Using SVM, the highest accuracy (0.96) was achieved from a single feature (mean of RED), or from three feature combinations of IVIM or ADC. Combining features from all models gave perfect classification. No single feature predicted HER2 status of ER + tumors (univariate or SVM), although high accuracy (0.90) was achieved with SVM combining several features. Importantly, these features had to include higher-order statistics (kurtosis and skewness), indicating the importance to account for heterogeneity. Our findings suggest that SVM, using features from a combination of diffusion models, improves prediction accuracy for differentiation of benign versus malignant breast tumors, and may further assist in subtyping of breast cancer. 3 Technical Efficacy: Stage 3 J. Magn. Reson. Imaging 2018;47:1205-1216. © 2017 International Society for Magnetic Resonance in Medicine.

Computer-aided classification of optical images for diagnosis of osteoarthritis in the finger joints.

PubMed

Zhang, Jiang; Wang, James Z; Yuan, Zhen; Sobel, Eric S; Jiang, Huabei

2011-01-01

This study presents a computer-aided classification method to distinguish osteoarthritis finger joints from healthy ones based on the functional images captured by x-ray guided diffuse optical tomography. Three imaging features, joint space width, optical absorption, and scattering coefficients, are employed to train a Least Squares Support Vector Machine (LS-SVM) classifier for osteoarthritis classification. The 10-fold validation results show that all osteoarthritis joints are clearly identified and all healthy joints are ruled out by the LS-SVM classifier. The best sensitivity, specificity, and overall accuracy of the classification by experienced technicians based on manual calculation of optical properties and visual examination of optical images are only 85%, 93%, and 90%, respectively. Therefore, our LS-SVM based computer-aided classification is a considerably improved method for osteoarthritis diagnosis.

Ensemble of sparse classifiers for high-dimensional biological data.

PubMed

Kim, Sunghan; Scalzo, Fabien; Telesca, Donatello; Hu, Xiao

2015-01-01

Biological data are often high in dimension while the number of samples is small. In such cases, the performance of classification can be improved by reducing the dimension of data, which is referred to as feature selection. Recently, a novel feature selection method has been proposed utilising the sparsity of high-dimensional biological data where a small subset of features accounts for most variance of the dataset. In this study we propose a new classification method for high-dimensional biological data, which performs both feature selection and classification within a single framework. Our proposed method utilises a sparse linear solution technique and the bootstrap aggregating algorithm. We tested its performance on four public mass spectrometry cancer datasets along with two other conventional classification techniques such as Support Vector Machines and Adaptive Boosting. The results demonstrate that our proposed method performs more accurate classification across various cancer datasets than those conventional classification techniques.

Comparison of hand-craft feature based SVM and CNN based deep learning framework for automatic polyp classification.

PubMed

Younghak Shin; Balasingham, Ilangko

2017-07-01

Colonoscopy is a standard method for screening polyps by highly trained physicians. Miss-detected polyps in colonoscopy are potential risk factor for colorectal cancer. In this study, we investigate an automatic polyp classification framework. We aim to compare two different approaches named hand-craft feature method and convolutional neural network (CNN) based deep learning method. Combined shape and color features are used for hand craft feature extraction and support vector machine (SVM) method is adopted for classification. For CNN approach, three convolution and pooling based deep learning framework is used for classification purpose. The proposed framework is evaluated using three public polyp databases. From the experimental results, we have shown that the CNN based deep learning framework shows better classification performance than the hand-craft feature based methods. It achieves over 90% of classification accuracy, sensitivity, specificity and precision.

Machine learning modelling for predicting soil liquefaction susceptibility

NASA Astrophysics Data System (ADS)

Samui, P.; Sitharam, T. G.

2011-01-01

This study describes two machine learning techniques applied to predict liquefaction susceptibility of soil based on the standard penetration test (SPT) data from the 1999 Chi-Chi, Taiwan earthquake. The first machine learning technique which uses Artificial Neural Network (ANN) based on multi-layer perceptions (MLP) that are trained with Levenberg-Marquardt backpropagation algorithm. The second machine learning technique uses the Support Vector machine (SVM) that is firmly based on the theory of statistical learning theory, uses classification technique. ANN and SVM have been developed to predict liquefaction susceptibility using corrected SPT [(N1)60] and cyclic stress ratio (CSR). Further, an attempt has been made to simplify the models, requiring only the two parameters [(N1)60 and peck ground acceleration (amax/g)], for the prediction of liquefaction susceptibility. The developed ANN and SVM models have also been applied to different case histories available globally. The paper also highlights the capability of the SVM over the ANN models.

New Image-Based Techniques for Prostate Biopsy and Treatment

DTIC Science & Technology

2012-04-01

C-arm fluoroscopy, MICCAI 2011, Toronto, Canada, 2011. 4) Poster Presentation: Prostate Cancer Probability Estimation Based on DCE- DTI Features...and P. Kozlowski, “Prostate Cancer Probability Estimation Based on DCE- DTI Features and Support Vector Machine Classification,” Annual Meeting of... DTI ), which characterize the de-phasing of the MR signal caused by molecular diffusion. Prostate cancer causes a pathological change in the tissue

Exploitation of RF-DNA for Device Classification and Verification Using GRLVQI Processing

DTIC Science & Technology

2012-12-01

5 FLD Fisher’s Linear Discriminant . . . . . . . . . . . . . . . . . . . 6 kNN K-Nearest Neighbor...Neighbor ( kNN ), Support Vector Machine (SVM), and simple cross-correlation techniques [40, 57, 82, 88, 94, 95]. The RF-DNA fingerprinting research in...Expansion and the Dis- crete Gabor Transform on a Non-Separable Lattice”. 2000 IEEE Int’l Conf on Acoustics, Speech , and Signal Processing (ICASSP00

[Discrimination of varieties of borneol using terahertz spectra based on principal component analysis and support vector machine].

PubMed

Li, Wu; Hu, Bing; Wang, Ming-wei

2014-12-01

In the present paper, the terahertz time-domain spectroscopy (THz-TDS) identification model of borneol based on principal component analysis (PCA) and support vector machine (SVM) was established. As one Chinese common agent, borneol needs a rapid, simple and accurate detection and identification method for its different source and being easily confused in the pharmaceutical and trade links. In order to assure the quality of borneol product and guard the consumer's right, quickly, efficiently and correctly identifying borneol has significant meaning to the production and transaction of borneol. Terahertz time-domain spectroscopy is a new spectroscopy approach to characterize material using terahertz pulse. The absorption terahertz spectra of blumea camphor, borneol camphor and synthetic borneol were measured in the range of 0.2 to 2 THz with the transmission THz-TDS. The PCA scores of 2D plots (PC1 X PC2) and 3D plots (PC1 X PC2 X PC3) of three kinds of borneol samples were obtained through PCA analysis, and both of them have good clustering effect on the 3 different kinds of borneol. The value matrix of the first 10 principal components (PCs) was used to replace the original spectrum data, and the 60 samples of the three kinds of borneol were trained and then the unknown 60 samples were identified. Four kinds of support vector machine model of different kernel functions were set up in this way. Results show that the accuracy of identification and classification of SVM RBF kernel function for three kinds of borneol is 100%, and we selected the SVM with the radial basis kernel function to establish the borneol identification model, in addition, in the noisy case, the classification accuracy rates of four SVM kernel function are above 85%, and this indicates that SVM has strong generalization ability. This study shows that PCA with SVM method of borneol terahertz spectroscopy has good classification and identification effects, and provides a new method for species identification of borneol in Chinese medicine.

Support Vector Machine and Artificial Neural Network Models for the Classification of Grapevine Varieties Using a Portable NIR Spectrophotometer.

PubMed

Gutiérrez, Salvador; Tardaguila, Javier; Fernández-Novales, Juan; Diago, María P

2015-01-01

The identification of different grapevine varieties, currently attended using visual ampelometry, DNA analysis and very recently, by hyperspectral analysis under laboratory conditions, is an issue of great importance in the wine industry. This work presents support vector machine and artificial neural network's modelling for grapevine varietal classification from in-field leaf spectroscopy. Modelling was attempted at two scales: site-specific and a global scale. Spectral measurements were obtained on the near-infrared (NIR) spectral range between 1600 to 2400 nm under field conditions in a non-destructive way using a portable spectrophotometer. For the site specific approach, spectra were collected from the adaxial side of 400 individual leaves of 20 grapevine (Vitis vinifera L.) varieties one week after veraison. For the global model, two additional sets of spectra were collected one week before harvest from two different vineyards in another vintage, each one consisting on 48 measurement from individual leaves of six varieties. Several combinations of spectra scatter correction and smoothing filtering were studied. For the training of the models, support vector machines and artificial neural networks were employed using the pre-processed spectra as input and the varieties as the classes of the models. The results from the pre-processing study showed that there was no influence whether using scatter correction or not. Also, a second-degree derivative with a window size of 5 Savitzky-Golay filtering yielded the highest outcomes. For the site-specific model, with 20 classes, the best results from the classifiers thrown an overall score of 87.25% of correctly classified samples. These results were compared under the same conditions with a model trained using partial least squares discriminant analysis, which showed a worse performance in every case. For the global model, a 6-class dataset involving samples from three different vineyards, two years and leaves monitored at post-veraison and harvest was also built up, reaching a 77.08% of correctly classified samples. The outcomes obtained demonstrate the capability of using a reliable method for fast, in-field, non-destructive grapevine varietal classification that could be very useful in viticulture and wine industry, either global or site-specific.

Real Time Monitoring System of Pollution Waste on Musi River Using Support Vector Machine (SVM) Method

NASA Astrophysics Data System (ADS)

Fachrurrozi, Muhammad; Saparudin; Erwin

2017-04-01

Real-time Monitoring and early detection system which measures the quality standard of waste in Musi River, Palembang, Indonesia is a system for determining air and water pollution level. This system was designed in order to create an integrated monitoring system and provide real time information that can be read. It is designed to measure acidity and water turbidity polluted by industrial waste, as well as to show and provide conditional data integrated in one system. This system consists of inputting and processing the data, and giving output based on processed data. Turbidity, substances, and pH sensor is used as a detector that produce analog electrical direct current voltage (DC). Early detection system works by determining the value of the ammonia threshold, acidity, and turbidity level of water in Musi River. The results is then presented based on the level group pollution by the Support Vector Machine classification method.

Hybrid Radar Emitter Recognition Based on Rough k-Means Classifier and Relevance Vector Machine

PubMed Central

Yang, Zhutian; Wu, Zhilu; Yin, Zhendong; Quan, Taifan; Sun, Hongjian

2013-01-01

Due to the increasing complexity of electromagnetic signals, there exists a significant challenge for recognizing radar emitter signals. In this paper, a hybrid recognition approach is presented that classifies radar emitter signals by exploiting the different separability of samples. The proposed approach comprises two steps, namely the primary signal recognition and the advanced signal recognition. In the former step, a novel rough k-means classifier, which comprises three regions, i.e., certain area, rough area and uncertain area, is proposed to cluster the samples of radar emitter signals. In the latter step, the samples within the rough boundary are used to train the relevance vector machine (RVM). Then RVM is used to recognize the samples in the uncertain area; therefore, the classification accuracy is improved. Simulation results show that, for recognizing radar emitter signals, the proposed hybrid recognition approach is more accurate, and presents lower computational complexity than traditional approaches. PMID:23344380

Uncertainty Management for Diagnostics and Prognostics of Batteries using Bayesian Techniques

NASA Technical Reports Server (NTRS)

Saha, Bhaskar; Goebel, kai

2007-01-01

Uncertainty management has always been the key hurdle faced by diagnostics and prognostics algorithms. A Bayesian treatment of this problem provides an elegant and theoretically sound approach to the modern Condition- Based Maintenance (CBM)/Prognostic Health Management (PHM) paradigm. The application of the Bayesian techniques to regression and classification in the form of Relevance Vector Machine (RVM), and to state estimation as in Particle Filters (PF), provides a powerful tool to integrate the diagnosis and prognosis of battery health. The RVM, which is a Bayesian treatment of the Support Vector Machine (SVM), is used for model identification, while the PF framework uses the learnt model, statistical estimates of noise and anticipated operational conditions to provide estimates of remaining useful life (RUL) in the form of a probability density function (PDF). This type of prognostics generates a significant value addition to the management of any operation involving electrical systems.

Morphological analysis of dendrites and spines by hybridization of ridge detection with twin support vector machine.

PubMed

Wang, Shuihua; Chen, Mengmeng; Li, Yang; Shao, Ying; Zhang, Yudong; Du, Sidan; Wu, Jane

2016-01-01

Dendritic spines are described as neuronal protrusions. The morphology of dendritic spines and dendrites has a strong relationship to its function, as well as playing an important role in understanding brain function. Quantitative analysis of dendrites and dendritic spines is essential to an understanding of the formation and function of the nervous system. However, highly efficient tools for the quantitative analysis of dendrites and dendritic spines are currently undeveloped. In this paper we propose a novel three-step cascaded algorithm-RTSVM- which is composed of ridge detection as the curvature structure identifier for backbone extraction, boundary location based on differences in density, the Hu moment as features and Twin Support Vector Machine (TSVM) classifiers for spine classification. Our data demonstrates that this newly developed algorithm has performed better than other available techniques used to detect accuracy and false alarm rates. This algorithm will be used effectively in neuroscience research.

Content-Based Discovery for Web Map Service using Support Vector Machine and User Relevance Feedback

PubMed Central

Cheng, Xiaoqiang; Qi, Kunlun; Zheng, Jie; You, Lan; Wu, Huayi

2016-01-01

Many discovery methods for geographic information services have been proposed. There are approaches for finding and matching geographic information services, methods for constructing geographic information service classification schemes, and automatic geographic information discovery. Overall, the efficiency of the geographic information discovery keeps improving., There are however, still two problems in Web Map Service (WMS) discovery that must be solved. Mismatches between the graphic contents of a WMS and the semantic descriptions in the metadata make discovery difficult for human users. End-users and computers comprehend WMSs differently creating semantic gaps in human-computer interactions. To address these problems, we propose an improved query process for WMSs based on the graphic contents of WMS layers, combining Support Vector Machine (SVM) and user relevance feedback. Our experiments demonstrate that the proposed method can improve the accuracy and efficiency of WMS discovery. PMID:27861505

Content-Based Discovery for Web Map Service using Support Vector Machine and User Relevance Feedback.

PubMed

Hu, Kai; Gui, Zhipeng; Cheng, Xiaoqiang; Qi, Kunlun; Zheng, Jie; You, Lan; Wu, Huayi

2016-01-01

Many discovery methods for geographic information services have been proposed. There are approaches for finding and matching geographic information services, methods for constructing geographic information service classification schemes, and automatic geographic information discovery. Overall, the efficiency of the geographic information discovery keeps improving., There are however, still two problems in Web Map Service (WMS) discovery that must be solved. Mismatches between the graphic contents of a WMS and the semantic descriptions in the metadata make discovery difficult for human users. End-users and computers comprehend WMSs differently creating semantic gaps in human-computer interactions. To address these problems, we propose an improved query process for WMSs based on the graphic contents of WMS layers, combining Support Vector Machine (SVM) and user relevance feedback. Our experiments demonstrate that the proposed method can improve the accuracy and efficiency of WMS discovery.

Support Vector Machines to improve physiologic hot flash measures: application to the ambulatory setting.

PubMed

Thurston, Rebecca C; Hernandez, Javier; Del Rio, Jose M; De La Torre, Fernando

2011-07-01

Most midlife women have hot flashes. The conventional criterion (≥2 μmho rise/30 s) for classifying hot flashes physiologically has shown poor performance. We improved this performance in the laboratory with Support Vector Machines (SVMs), a pattern classification method. We aimed to compare conventional to SVM methods to classify hot flashes in the ambulatory setting. Thirty-one women with hot flashes underwent 24 h of ambulatory sternal skin conductance monitoring. Hot flashes were quantified with conventional (≥2 μmho/30 s) and SVM methods. Conventional methods had low sensitivity (sensitivity=.57, specificity=.98, positive predictive value (PPV)=.91, negative predictive value (NPV)=.90, F1=.60), with performance lower with higher body mass index (BMI). SVMs improved this performance (sensitivity=.87, specificity=.97, PPV=.90, NPV=.96, F1=.88) and reduced BMI variation. SVMs can improve ambulatory physiologic hot flash measures. Copyright © 2010 Society for Psychophysiological Research.

A Method for Extracting Important Segments from Documents Using Support Vector Machines

NASA Astrophysics Data System (ADS)

Suzuki, Daisuke; Utsumi, Akira

In this paper we propose an extraction-based method for automatic summarization. The proposed method consists of two processes: important segment extraction and sentence compaction. The process of important segment extraction classifies each segment in a document as important or not by Support Vector Machines (SVMs). The process of sentence compaction then determines grammatically appropriate portions of a sentence for a summary according to its dependency structure and the classification result by SVMs. To test the performance of our method, we conducted an evaluation experiment using the Text Summarization Challenge (TSC-1) corpus of human-prepared summaries. The result was that our method achieved better performance than a segment-extraction-only method and the Lead method, especially for sentences only a part of which was included in human summaries. Further analysis of the experimental results suggests that a hybrid method that integrates sentence extraction with segment extraction may generate better summaries.

Classification of octet AB-type binary compounds using dynamical charges: A materials informatics perspective

DOE PAGES

Pilania, G.; Gubernatis, J. E.; Lookman, T.

2015-12-03

The role of dynamical (or Born effective) charges in classification of octet AB-type binary compounds between four-fold (zincblende/wurtzite crystal structures) and six-fold (rocksalt crystal structure) coordinated systems is discussed. We show that the difference in the dynamical charges of the fourfold and sixfold coordinated structures, in combination with Harrison’s polarity, serves as an excellent feature to classify the coordination of 82 sp–bonded binary octet compounds. We use a support vector machine classifier to estimate the average classification accuracy and the associated variance in our model where a decision boundary is learned in a supervised manner. Lastly, we compare the out-of-samplemore » classification accuracy achieved by our feature pair with those reported previously.« less

Camouflage target reconnaissance based on hyperspectral imaging technology

NASA Astrophysics Data System (ADS)

Hua, Wenshen; Guo, Tong; Liu, Xun

2015-08-01

Efficient camouflaged target reconnaissance technology makes great influence on modern warfare. Hyperspectral images can provide large spectral range and high spectral resolution, which are invaluable in discriminating between camouflaged targets and backgrounds. Hyperspectral target detection and classification technology are utilized to achieve single class and multi-class camouflaged targets reconnaissance respectively. Constrained energy minimization (CEM), a widely used algorithm in hyperspectral target detection, is employed to achieve one class camouflage target reconnaissance. Then, support vector machine (SVM), a classification method, is proposed to achieve multi-class camouflage target reconnaissance. Experiments have been conducted to demonstrate the efficiency of the proposed method.

Is it worth changing pattern recognition methods for structural health monitoring?

NASA Astrophysics Data System (ADS)

Bull, L. A.; Worden, K.; Cross, E. J.; Dervilis, N.

2017-05-01

The key element of this work is to demonstrate alternative strategies for using pattern recognition algorithms whilst investigating structural health monitoring. This paper looks to determine if it makes any difference in choosing from a range of established classification techniques: from decision trees and support vector machines, to Gaussian processes. Classification algorithms are tested on adjustable synthetic data to establish performance metrics, then all techniques are applied to real SHM data. To aid the selection of training data, an informative chain of artificial intelligence tools is used to explore an active learning interaction between meaningful clusters of data.

A method of neighbor classes based SVM classification for optical printed Chinese character recognition.

PubMed

Zhang, Jie; Wu, Xiaohong; Yu, Yanmei; Luo, Daisheng

2013-01-01

In optical printed Chinese character recognition (OPCCR), many classifiers have been proposed for the recognition. Among the classifiers, support vector machine (SVM) might be the best classifier. However, SVM is a classifier for two classes. When it is used for multi-classes in OPCCR, its computation is time-consuming. Thus, we propose a neighbor classes based SVM (NC-SVM) to reduce the computation consumption of SVM. Experiments of NC-SVM classification for OPCCR have been done. The results of the experiments have shown that the NC-SVM we proposed can effectively reduce the computation time in OPCCR.

On the detection of pornographic digital images

NASA Astrophysics Data System (ADS)

Schettini, Raimondo; Brambilla, Carla; Cusano, Claudio; Ciocca, Gianluigi

2003-06-01

The paper addresses the problem of distinguishing between pornographic and non-pornographic photographs, for the design of semantic filters for the web. Both, decision forests of trees built according to CART (Classification And Regression Trees) methodology and Support Vectors Machines (SVM), have been used to perform the classification. The photographs are described by a set of low-level features, features that can be automatically computed simply on gray-level and color representation of the image. The database used in our experiments contained 1500 photographs, 750 of which labeled as pornographic on the basis of the independent judgement of several viewers.

Prediction of cell penetrating peptides by support vector machines.

PubMed

Sanders, William S; Johnston, C Ian; Bridges, Susan M; Burgess, Shane C; Willeford, Kenneth O

2011-07-01

Cell penetrating peptides (CPPs) are those peptides that can transverse cell membranes to enter cells. Once inside the cell, different CPPs can localize to different cellular components and perform different roles. Some generate pore-forming complexes resulting in the destruction of cells while others localize to various organelles. Use of machine learning methods to predict potential new CPPs will enable more rapid screening for applications such as drug delivery. We have investigated the influence of the composition of training datasets on the ability to classify peptides as cell penetrating using support vector machines (SVMs). We identified 111 known CPPs and 34 known non-penetrating peptides from the literature and commercial vendors and used several approaches to build training data sets for the classifiers. Features were calculated from the datasets using a set of basic biochemical properties combined with features from the literature determined to be relevant in the prediction of CPPs. Our results using different training datasets confirm the importance of a balanced training set with approximately equal number of positive and negative examples. The SVM based classifiers have greater classification accuracy than previously reported methods for the prediction of CPPs, and because they use primary biochemical properties of the peptides as features, these classifiers provide insight into the properties needed for cell-penetration. To confirm our SVM classifications, a subset of peptides classified as either penetrating or non-penetrating was selected for synthesis and experimental validation. Of the synthesized peptides predicted to be CPPs, 100% of these peptides were shown to be penetrating.

Separating generalized anxiety disorder from major depression using clinical, hormonal, and structural MRI data: A multimodal machine learning study.

PubMed

Hilbert, Kevin; Lueken, Ulrike; Muehlhan, Markus; Beesdo-Baum, Katja

2017-03-01

Generalized anxiety disorder (GAD) is difficult to recognize and hard to separate from major depression (MD) in clinical settings. Biomarkers might support diagnostic decisions. This study used machine learning on multimodal biobehavioral data from a sample of GAD, MD and healthy subjects to differentiate subjects with a disorder from healthy subjects (case-classification) and to differentiate GAD from MD (disorder-classification). Subjects with GAD ( n  = 19), MD without GAD ( n  = 14), and healthy comparison subjects ( n  = 24) were included. The sample was matched regarding age, sex, handedness and education and free of psychopharmacological medication. Binary support vector machines were used within a nested leave-one-out cross-validation framework. Clinical questionnaires, cortisol release, gray matter (GM), and white matter (WM) volumes were used as input data separately and in combination. Questionnaire data were well-suited for case-classification but not disorder-classification (accuracies: 96.40%, p  < .001; 56.58%, p  > .22). The opposite pattern was found for imaging data (case-classification GM/WM: 58.71%, p  = .09/43.18%, p  > .66; disorder-classification GM/WM: 68.05%, p  = .034/58.27%, p  > .15) and for cortisol data (38.02%, p  = .84; 74.60%, p  = .009). All data combined achieved 90.10% accuracy ( p  < .001) for case-classification and 67.46% accuracy ( p  = .0268) for disorder-classification. In line with previous evidence, classification of GAD was difficult using clinical questionnaire data alone. Particularly cortisol and GM volume data were able to provide incremental value for the classification of GAD. Findings suggest that neurobiological biomarkers are a useful target for further research to delineate their potential contribution to diagnostic processes.

Comparison of machine learning and semi-quantification algorithms for (I123)FP-CIT classification: the beginning of the end for semi-quantification?

PubMed

Taylor, Jonathan Christopher; Fenner, John Wesley

2017-11-29

Semi-quantification methods are well established in the clinic for assisted reporting of (I123) Ioflupane images. Arguably, these are limited diagnostic tools. Recent research has demonstrated the potential for improved classification performance offered by machine learning algorithms. A direct comparison between methods is required to establish whether a move towards widespread clinical adoption of machine learning algorithms is justified. This study compared three machine learning algorithms with that of a range of semi-quantification methods, using the Parkinson's Progression Markers Initiative (PPMI) research database and a locally derived clinical database for validation. Machine learning algorithms were based on support vector machine classifiers with three different sets of features: Voxel intensities Principal components of image voxel intensities Striatal binding radios from the putamen and caudate. Semi-quantification methods were based on striatal binding ratios (SBRs) from both putamina, with and without consideration of the caudates. Normal limits for the SBRs were defined through four different methods: Minimum of age-matched controls Mean minus 1/1.5/2 standard deviations from age-matched controls Linear regression of normal patient data against age (minus 1/1.5/2 standard errors) Selection of the optimum operating point on the receiver operator characteristic curve from normal and abnormal training data Each machine learning and semi-quantification technique was evaluated with stratified, nested 10-fold cross-validation, repeated 10 times. The mean accuracy of the semi-quantitative methods for classification of local data into Parkinsonian and non-Parkinsonian groups varied from 0.78 to 0.87, contrasting with 0.89 to 0.95 for classifying PPMI data into healthy controls and Parkinson's disease groups. The machine learning algorithms gave mean accuracies between 0.88 to 0.92 and 0.95 to 0.97 for local and PPMI data respectively. Classification performance was lower for the local database than the research database for both semi-quantitative and machine learning algorithms. However, for both databases, the machine learning methods generated equal or higher mean accuracies (with lower variance) than any of the semi-quantification approaches. The gain in performance from using machine learning algorithms as compared to semi-quantification was relatively small and may be insufficient, when considered in isolation, to offer significant advantages in the clinical context.

Epileptic seizure detection in EEG signal with GModPCA and support vector machine.

PubMed

Jaiswal, Abeg Kumar; Banka, Haider

2017-01-01

Epilepsy is one of the most common neurological disorders caused by recurrent seizures. Electroencephalograms (EEGs) record neural activity and can detect epilepsy. Visual inspection of an EEG signal for epileptic seizure detection is a time-consuming process and may lead to human error; therefore, recently, a number of automated seizure detection frameworks were proposed to replace these traditional methods. Feature extraction and classification are two important steps in these procedures. Feature extraction focuses on finding the informative features that could be used for classification and correct decision-making. Therefore, proposing effective feature extraction techniques for seizure detection is of great significance. Principal Component Analysis (PCA) is a dimensionality reduction technique used in different fields of pattern recognition including EEG signal classification. Global modular PCA (GModPCA) is a variation of PCA. In this paper, an effective framework with GModPCA and Support Vector Machine (SVM) is presented for epileptic seizure detection in EEG signals. The feature extraction is performed with GModPCA, whereas SVM trained with radial basis function kernel performed the classification between seizure and nonseizure EEG signals. Seven different experimental cases were conducted on the benchmark epilepsy EEG dataset. The system performance was evaluated using 10-fold cross-validation. In addition, we prove analytically that GModPCA has less time and space complexities as compared to PCA. The experimental results show that EEG signals have strong inter-sub-pattern correlations. GModPCA and SVM have been able to achieve 100% accuracy for the classification between normal and epileptic signals. Along with this, seven different experimental cases were tested. The classification results of the proposed approach were better than were compared the results of some of the existing methods proposed in literature. It is also found that the time and space complexities of GModPCA are less as compared to PCA. This study suggests that GModPCA and SVM could be used for automated epileptic seizure detection in EEG signal.

Using Support Vector Machine to identify imaging biomarkers of neurological and psychiatric disease: a critical review.

PubMed

Orrù, Graziella; Pettersson-Yeo, William; Marquand, Andre F; Sartori, Giuseppe; Mechelli, Andrea

2012-04-01

Standard univariate analysis of neuroimaging data has revealed a host of neuroanatomical and functional differences between healthy individuals and patients suffering a wide range of neurological and psychiatric disorders. Significant only at group level however these findings have had limited clinical translation, and recent attention has turned toward alternative forms of analysis, including Support-Vector-Machine (SVM). A type of machine learning, SVM allows categorisation of an individual's previously unseen data into a predefined group using a classification algorithm, developed on a training data set. In recent years, SVM has been successfully applied in the context of disease diagnosis, transition prediction and treatment prognosis, using both structural and functional neuroimaging data. Here we provide a brief overview of the method and review those studies that applied it to the investigation of Alzheimer's disease, schizophrenia, major depression, bipolar disorder, presymptomatic Huntington's disease, Parkinson's disease and autistic spectrum disorder. We conclude by discussing the main theoretical and practical challenges associated with the implementation of this method into the clinic and possible future directions. Copyright © 2012 Elsevier Ltd. All rights reserved.

Integrating support vector machines and random forests to classify crops in time series of Worldview-2 images

NASA Astrophysics Data System (ADS)

Zafari, A.; Zurita-Milla, R.; Izquierdo-Verdiguier, E.

2017-10-01

Crop maps are essential inputs for the agricultural planning done at various governmental and agribusinesses agencies. Remote sensing offers timely and costs efficient technologies to identify and map crop types over large areas. Among the plethora of classification methods, Support Vector Machine (SVM) and Random Forest (RF) are widely used because of their proven performance. In this work, we study the synergic use of both methods by introducing a random forest kernel (RFK) in an SVM classifier. A time series of multispectral WorldView-2 images acquired over Mali (West Africa) in 2014 was used to develop our case study. Ground truth containing five common crop classes (cotton, maize, millet, peanut, and sorghum) were collected at 45 farms and used to train and test the classifiers. An SVM with the standard Radial Basis Function (RBF) kernel, a RF, and an SVM-RFK were trained and tested over 10 random training and test subsets generated from the ground data. Results show that the newly proposed SVM-RFK classifier can compete with both RF and SVM-RBF. The overall accuracies based on the spectral bands only are of 83, 82 and 83% respectively. Adding vegetation indices to the analysis result in the classification accuracy of 82, 81 and 84% for SVM-RFK, RF, and SVM-RBF respectively. Overall, it can be observed that the newly tested RFK can compete with SVM-RBF and RF classifiers in terms of classification accuracy.

The Low Backscattering Objects Classification in Polsar Image Based on Bag of Words Model Using Support Vector Machine

NASA Astrophysics Data System (ADS)

Yang, L.; Shi, L.; Li, P.; Yang, J.; Zhao, L.; Zhao, B.

2018-04-01

Due to the forward scattering and block of radar signal, the water, bare soil, shadow, named low backscattering objects (LBOs), often present low backscattering intensity in polarimetric synthetic aperture radar (PolSAR) image. Because the LBOs rise similar backscattering intensity and polarimetric responses, the spectral-based classifiers are inefficient to deal with LBO classification, such as Wishart method. Although some polarimetric features had been exploited to relieve the confusion phenomenon, the backscattering features are still found unstable when the system noise floor varies in the range direction. This paper will introduce a simple but effective scene classification method based on Bag of Words (BoW) model using Support Vector Machine (SVM) to discriminate the LBOs, without relying on any polarimetric features. In the proposed approach, square windows are firstly opened around the LBOs adaptively to determine the scene images, and then the Scale-Invariant Feature Transform (SIFT) points are detected in training and test scenes. The several SIFT features detected are clustered using K-means to obtain certain cluster centers as the visual word lists and scene images are represented using word frequency. At last, the SVM is selected for training and predicting new scenes as some kind of LBOs. The proposed method is executed over two AIRSAR data sets at C band and L band, including water, bare soil and shadow scenes. The experimental results illustrate the effectiveness of the scene method in distinguishing LBOs.

Discrimination of raw and processed Dipsacus asperoides by near infrared spectroscopy combined with least squares-support vector machine and random forests

NASA Astrophysics Data System (ADS)

Xin, Ni; Gu, Xiao-Feng; Wu, Hao; Hu, Yu-Zhu; Yang, Zhong-Lin

2012-04-01

Most herbal medicines could be processed to fulfill the different requirements of therapy. The purpose of this study was to discriminate between raw and processed Dipsacus asperoides, a common traditional Chinese medicine, based on their near infrared (NIR) spectra. Least squares-support vector machine (LS-SVM) and random forests (RF) were employed for full-spectrum classification. Three types of kernels, including linear kernel, polynomial kernel and radial basis function kernel (RBF), were checked for optimization of LS-SVM model. For comparison, a linear discriminant analysis (LDA) model was performed for classification, and the successive projections algorithm (SPA) was executed prior to building an LDA model to choose an appropriate subset of wavelengths. The three methods were applied to a dataset containing 40 raw herbs and 40 corresponding processed herbs. We ran 50 runs of 10-fold cross validation to evaluate the model's efficiency. The performance of the LS-SVM with RBF kernel (RBF LS-SVM) was better than the other two kernels. The RF, RBF LS-SVM and SPA-LDA successfully classified all test samples. The mean error rates for the 50 runs of 10-fold cross validation were 1.35% for RBF LS-SVM, 2.87% for RF, and 2.50% for SPA-LDA. The best classification results were obtained by using LS-SVM with RBF kernel, while RF was fast in the training and making predictions.

Prediction of pediatric unipolar depression using multiple neuromorphometric measurements: a pattern classification approach.

PubMed

Wu, Mon-Ju; Wu, Hanjing Emily; Mwangi, Benson; Sanches, Marsal; Selvaraj, Sudhakar; Zunta-Soares, Giovana B; Soares, Jair C

2015-03-01

Diagnosis of pediatric neuropsychiatric disorders such as unipolar depression is largely based on clinical judgment - without objective biomarkers to guide diagnostic process and subsequent therapeutic interventions. Neuroimaging studies have previously reported average group-level neuroanatomical differences between patients with pediatric unipolar depression and healthy controls. In the present study, we investigated the utility of multiple neuromorphometric indices in distinguishing pediatric unipolar depression patients from healthy controls at an individual subject level. We acquired structural T1-weighted scans from 25 pediatric unipolar depression patients and 26 demographically matched healthy controls. Multiple neuromorphometric indices such as cortical thickness, volume, and cortical folding patterns were obtained. A support vector machine pattern classification model was 'trained' to distinguish individual subjects with pediatric unipolar depression from healthy controls based on multiple neuromorphometric indices and model predictive validity (sensitivity and specificity) calculated. The model correctly identified 40 out of 51 subjects translating to 78.4% accuracy, 76.0% sensitivity and 80.8% specificity, chi-square p-value = 0.000049. Volumetric and cortical folding abnormalities in the right thalamus and right temporal pole respectively were most central in distinguishing individual patients with pediatric unipolar depression from healthy controls. These findings provide evidence that a support vector machine pattern classification model using multiple neuromorphometric indices may qualify as diagnostic marker for pediatric unipolar depression. In addition, our results identified the most relevant neuromorphometric features in distinguishing PUD patients from healthy controls. Copyright © 2015 Elsevier Ltd. All rights reserved.

A machine learning approach for classification of anatomical coverage in CT

NASA Astrophysics Data System (ADS)

Wang, Xiaoyong; Lo, Pechin; Ramakrishna, Bharath; Goldin, Johnathan; Brown, Matthew

2016-03-01

Automatic classification of anatomical coverage of medical images is critical for big data mining and as a pre-processing step to automatically trigger specific computer aided diagnosis systems. The traditional way to identify scans through DICOM headers has various limitations due to manual entry of series descriptions and non-standardized naming conventions. In this study, we present a machine learning approach where multiple binary classifiers were used to classify different anatomical coverages of CT scans. A one-vs-rest strategy was applied. For a given training set, a template scan was selected from the positive samples and all other scans were registered to it. Each registered scan was then evenly split into k × k × k non-overlapping blocks and for each block the mean intensity was computed. This resulted in a 1 × k3 feature vector for each scan. The feature vectors were then used to train a SVM based classifier. In this feasibility study, four classifiers were built to identify anatomic coverages of brain, chest, abdomen-pelvis, and chest-abdomen-pelvis CT scans. Each classifier was trained and tested using a set of 300 scans from different subjects, composed of 150 positive samples and 150 negative samples. Area under the ROC curve (AUC) of the testing set was measured to evaluate the performance in a two-fold cross validation setting. Our results showed good classification performance with an average AUC of 0.96.

Comparison of Machine Learning Methods for the Arterial Hypertension Diagnostics

PubMed Central

Belo, David; Gamboa, Hugo

2017-01-01

The paper presents results of machine learning approach accuracy applied analysis of cardiac activity. The study evaluates the diagnostics possibilities of the arterial hypertension by means of the short-term heart rate variability signals. Two groups were studied: 30 relatively healthy volunteers and 40 patients suffering from the arterial hypertension of II-III degree. The following machine learning approaches were studied: linear and quadratic discriminant analysis, k-nearest neighbors, support vector machine with radial basis, decision trees, and naive Bayes classifier. Moreover, in the study, different methods of feature extraction are analyzed: statistical, spectral, wavelet, and multifractal. All in all, 53 features were investigated. Investigation results show that discriminant analysis achieves the highest classification accuracy. The suggested approach of noncorrelated feature set search achieved higher results than data set based on the principal components. PMID:28831239

Improvements on ν-Twin Support Vector Machine.

PubMed

Khemchandani, Reshma; Saigal, Pooja; Chandra, Suresh

2016-07-01

In this paper, we propose two novel binary classifiers termed as "Improvements on ν-Twin Support Vector Machine: Iν-TWSVM and Iν-TWSVM (Fast)" that are motivated by ν-Twin Support Vector Machine (ν-TWSVM). Similar to ν-TWSVM, Iν-TWSVM determines two nonparallel hyperplanes such that they are closer to their respective classes and are at least ρ distance away from the other class. The significant advantage of Iν-TWSVM over ν-TWSVM is that Iν-TWSVM solves one smaller-sized Quadratic Programming Problem (QPP) and one Unconstrained Minimization Problem (UMP); as compared to solving two related QPPs in ν-TWSVM. Further, Iν-TWSVM (Fast) avoids solving a smaller sized QPP and transforms it as a unimodal function, which can be solved using line search methods and similar to Iν-TWSVM, the other problem is solved as a UMP. Due to their novel formulation, the proposed classifiers are faster than ν-TWSVM and have comparable generalization ability. Iν-TWSVM also implements structural risk minimization (SRM) principle by introducing a regularization term, along with minimizing the empirical risk. The other properties of Iν-TWSVM, related to support vectors (SVs), are similar to that of ν-TWSVM. To test the efficacy of the proposed method, experiments have been conducted on a wide range of UCI and a skewed variation of NDC datasets. We have also given the application of Iν-TWSVM as a binary classifier for pixel classification of color images. Copyright © 2016 Elsevier Ltd. All rights reserved.

Combining information from 3 anatomic regions in the diagnosis of glaucoma with time-domain optical coherence tomography.

PubMed

Wang, Mingwu; Lu, Ake Tzu-Hui; Varma, Rohit; Schuman, Joel S; Greenfield, David S; Huang, David

2014-03-01

To improve the diagnosis of glaucoma by combining time-domain optical coherence tomography (TD-OCT) measurements of the optic disc, circumpapillary retinal nerve fiber layer (RNFL), and macular retinal thickness. Ninety-six age-matched normal and 96 perimetric glaucoma participants were included in this observational, cross-sectional study. Or-logic, support vector machine, relevance vector machine, and linear discrimination function were used to analyze the performances of combined TD-OCT diagnostic variables. The area under the receiver-operating curve (AROC) was used to evaluate the diagnostic accuracy and to compare the diagnostic performance of single and combined anatomic variables. The best RNFL thickness variables were the inferior (AROC=0.900), overall (AROC=0.892), and superior quadrants (AROC=0.850). The best optic disc variables were horizontal integrated rim width (AROC=0.909), vertical integrated rim area (AROC=0.908), and cup/disc vertical ratio (AROC=0.890). All macular retinal thickness variables had AROCs of 0.829 or less. Combining the top 3 RNFL and optic disc variables in optimizing glaucoma diagnosis, support vector machine had the highest AROC, 0.954, followed by or-logic (AROC=0.946), linear discrimination function (AROC=0.946), and relevance vector machine (AROC=0.943). All combination diagnostic variables had significantly larger AROCs than any single diagnostic variable. There are no significant differences among the combination diagnostic indices. With TD-OCT, RNFL and optic disc variables had better diagnostic accuracy than macular retinal variables. Combining top RNFL and optic disc variables significantly improved diagnostic performance. Clinically, or-logic classification was the most practical analytical tool with sufficient accuracy to diagnose early glaucoma.

Object recognition through a multi-mode fiber

NASA Astrophysics Data System (ADS)

Takagi, Ryosuke; Horisaki, Ryoichi; Tanida, Jun

2017-04-01

We present a method of recognizing an object through a multi-mode fiber. A number of speckle patterns transmitted through a multi-mode fiber are provided to a classifier based on machine learning. We experimentally demonstrated binary classification of face and non-face targets based on the method. The measurement process of the experimental setup was random and nonlinear because a multi-mode fiber is a typical strongly scattering medium and any reference light was not used in our setup. Comparisons between three supervised learning methods, support vector machine, adaptive boosting, and neural network, are also provided. All of those learning methods achieved high accuracy rates at about 90% for the classification. The approach presented here can realize a compact and smart optical sensor. It is practically useful for medical applications, such as endoscopy. Also our study indicated a promising utilization of artificial intelligence, which has rapidly progressed, for reducing optical and computational costs in optical sensing systems.

Incremental Transductive Learning Approaches to Schistosomiasis Vector Classification

NASA Astrophysics Data System (ADS)

Fusco, Terence; Bi, Yaxin; Wang, Haiying; Browne, Fiona

2016-08-01

The key issues pertaining to collection of epidemic disease data for our analysis purposes are that it is a labour intensive, time consuming and expensive process resulting in availability of sparse sample data which we use to develop prediction models. To address this sparse data issue, we present the novel Incremental Transductive methods to circumvent the data collection process by applying previously acquired data to provide consistent, confidence-based labelling alternatives to field survey research. We investigated various reasoning approaches for semi-supervised machine learning including Bayesian models for labelling data. The results show that using the proposed methods, we can label instances of data with a class of vector density at a high level of confidence. By applying the Liberal and Strict Training Approaches, we provide a labelling and classification alternative to standalone algorithms. The methods in this paper are components in the process of reducing the proliferation of the Schistosomiasis disease and its effects.

Machine learning and dyslexia: Classification of individual structural neuro-imaging scans of students with and without dyslexia

PubMed Central

Tamboer, P.; Vorst, H.C.M.; Ghebreab, S.; Scholte, H.S.

2016-01-01

Meta-analytic studies suggest that dyslexia is characterized by subtle and spatially distributed variations in brain anatomy, although many variations failed to be significant after corrections of multiple comparisons. To circumvent issues of significance which are characteristic for conventional analysis techniques, and to provide predictive value, we applied a machine learning technique – support vector machine – to differentiate between subjects with and without dyslexia. In a sample of 22 students with dyslexia (20 women) and 27 students without dyslexia (25 women) (18–21 years), a classification performance of 80% (p < 0.001; d-prime = 1.67) was achieved on the basis of differences in gray matter (sensitivity 82%, specificity 78%). The voxels that were most reliable for classification were found in the left occipital fusiform gyrus (LOFG), in the right occipital fusiform gyrus (ROFG), and in the left inferior parietal lobule (LIPL). Additionally, we found that classification certainty (e.g. the percentage of times a subject was correctly classified) correlated with severity of dyslexia (r = 0.47). Furthermore, various significant correlations were found between the three anatomical regions and behavioural measures of spelling, phonology and whole-word-reading. No correlations were found with behavioural measures of short-term memory and visual/attentional confusion. These data indicate that the LOFG, ROFG and the LIPL are neuro-endophenotype and potentially biomarkers for types of dyslexia related to reading, spelling and phonology. In a second and independent sample of 876 young adults of a general population, the trained classifier of the first sample was tested, resulting in a classification performance of 59% (p = 0.07; d-prime = 0.65). This decline in classification performance resulted from a large percentage of false alarms. This study provided support for the use of machine learning in anatomical brain imaging. PMID:27114899

Improving galaxy morphologies for SDSS with Deep Learning

NASA Astrophysics Data System (ADS)

Domínguez Sánchez, H.; Huertas-Company, M.; Bernardi, M.; Tuccillo, D.; Fischer, J. L.

2018-05-01

We present a morphological catalogue for ˜670 000 galaxies in the Sloan Digital Sky Survey in two flavours: T-type, related to the Hubble sequence, and Galaxy Zoo 2 (GZ2 hereafter) classification scheme. By combining accurate existing visual classification catalogues with machine learning, we provide the largest and most accurate morphological catalogue up to date. The classifications are obtained with Deep Learning algorithms using Convolutional Neural Networks (CNNs). We use two visual classification catalogues, GZ2 and Nair & Abraham (2010), for training CNNs with colour images in order to obtain T-types and a series of GZ2 type questions (disc/features, edge-on galaxies, bar signature, bulge prominence, roundness, and mergers). We also provide an additional probability enabling a separation between pure elliptical (E) from S0, where the T-type model is not so efficient. For the T-type, our results show smaller offset and scatter than previous models trained with support vector machines. For the GZ2 type questions, our models have large accuracy (>97 per cent), precision and recall values (>90 per cent), when applied to a test sample with the same characteristics as the one used for training. The catalogue is publicly released with the paper.

Structural classification of proteins using texture descriptors extracted from the cellular automata image.

PubMed

Kavianpour, Hamidreza; Vasighi, Mahdi

2017-02-01

Nowadays, having knowledge about cellular attributes of proteins has an important role in pharmacy, medical science and molecular biology. These attributes are closely correlated with the function and three-dimensional structure of proteins. Knowledge of protein structural class is used by various methods for better understanding the protein functionality and folding patterns. Computational methods and intelligence systems can have an important role in performing structural classification of proteins. Most of protein sequences are saved in databanks as characters and strings and a numerical representation is essential for applying machine learning methods. In this work, a binary representation of protein sequences is introduced based on reduced amino acids alphabets according to surrounding hydrophobicity index. Many important features which are hidden in these long binary sequences can be clearly displayed through their cellular automata images. The extracted features from these images are used to build a classification model by support vector machine. Comparing to previous studies on the several benchmark datasets, the promising classification rates obtained by tenfold cross-validation imply that the current approach can help in revealing some inherent features deeply hidden in protein sequences and improve the quality of predicting protein structural class.

A Novel Fiber Optic Based Surveillance System for Prevention of Pipeline Integrity Threats.

PubMed

Tejedor, Javier; Macias-Guarasa, Javier; Martins, Hugo F; Piote, Daniel; Pastor-Graells, Juan; Martin-Lopez, Sonia; Corredera, Pedro; Gonzalez-Herraez, Miguel

2017-02-12

This paper presents a novel surveillance system aimed at the detection and classification of threats in the vicinity of a long gas pipeline. The sensing system is based on phase-sensitive optical time domain reflectometry ( ϕ -OTDR) technology for signal acquisition and pattern recognition strategies for threat identification. The proposal incorporates contextual information at the feature level and applies a system combination strategy for pattern classification. The contextual information at the feature level is based on the tandem approach (using feature representations produced by discriminatively-trained multi-layer perceptrons) by employing feature vectors that spread different temporal contexts. The system combination strategy is based on a posterior combination of likelihoods computed from different pattern classification processes. The system operates in two different modes: (1) machine + activity identification, which recognizes the activity being carried out by a certain machine, and (2) threat detection, aimed at detecting threats no matter what the real activity being conducted is. In comparison with a previous system based on the same rigorous experimental setup, the results show that the system combination from the contextual feature information improves the results for each individual class in both operational modes, as well as the overall classification accuracy, with statistically-significant improvements.

Semi-supervised manifold learning with affinity regularization for Alzheimer's disease identification using positron emission tomography imaging.

PubMed

Lu, Shen; Xia, Yong; Cai, Tom Weidong; Feng, David Dagan

2015-01-01

Dementia, Alzheimer's disease (AD) in particular is a global problem and big threat to the aging population. An image based computer-aided dementia diagnosis method is needed to providing doctors help during medical image examination. Many machine learning based dementia classification methods using medical imaging have been proposed and most of them achieve accurate results. However, most of these methods make use of supervised learning requiring fully labeled image dataset, which usually is not practical in real clinical environment. Using large amount of unlabeled images can improve the dementia classification performance. In this study we propose a new semi-supervised dementia classification method based on random manifold learning with affinity regularization. Three groups of spatial features are extracted from positron emission tomography (PET) images to construct an unsupervised random forest which is then used to regularize the manifold learning objective function. The proposed method, stat-of-the-art Laplacian support vector machine (LapSVM) and supervised SVM are applied to classify AD and normal controls (NC). The experiment results show that learning with unlabeled images indeed improves the classification performance. And our method outperforms LapSVM on the same dataset.

Unsupervised domain adaptation for early detection of drought stress in hyperspectral images

NASA Astrophysics Data System (ADS)

Schmitter, P.; Steinrücken, J.; Römer, C.; Ballvora, A.; Léon, J.; Rascher, U.; Plümer, L.

2017-09-01

Hyperspectral images can be used to uncover physiological processes in plants if interpreted properly. Machine Learning methods such as Support Vector Machines (SVM) and Random Forests have been applied to estimate development of biomass and detect and predict plant diseases and drought stress. One basic requirement of machine learning implies, that training and testing is done in the same domain and the same distribution. Different genotypes, environmental conditions, illumination and sensors violate this requirement in most practical circumstances. Here, we present an approach, which enables the detection of physiological processes by transferring the prior knowledge within an existing model into a related target domain, where no label information is available. We propose a two-step transformation of the target features, which enables a direct application of an existing model. The transformation is evaluated by an objective function including additional prior knowledge about classification and physiological processes in plants. We have applied the approach to three sets of hyperspectral images, which were acquired with different plant species in different environments observed with different sensors. It is shown, that a classification model, derived on one of the sets, delivers satisfying classification results on the transformed features of the other data sets. Furthermore, in all cases early non-invasive detection of drought stress was possible.

Skipping the real world: Classification of PolSAR images without explicit feature extraction

NASA Astrophysics Data System (ADS)

Hänsch, Ronny; Hellwich, Olaf

2018-06-01

The typical processing chain for pixel-wise classification from PolSAR images starts with an optional preprocessing step (e.g. speckle reduction), continues with extracting features projecting the complex-valued data into the real domain (e.g. by polarimetric decompositions) which are then used as input for a machine-learning based classifier, and ends in an optional postprocessing (e.g. label smoothing). The extracted features are usually hand-crafted as well as preselected and represent (a somewhat arbitrary) projection from the complex to the real domain in order to fit the requirements of standard machine-learning approaches such as Support Vector Machines or Artificial Neural Networks. This paper proposes to adapt the internal node tests of Random Forests to work directly on the complex-valued PolSAR data, which makes any explicit feature extraction obsolete. This approach leads to a classification framework with a significantly decreased computation time and memory footprint since no image features have to be computed and stored beforehand. The experimental results on one fully-polarimetric and one dual-polarimetric dataset show that, despite the simpler approach, accuracy can be maintained (decreased by only less than 2 % for the fully-polarimetric dataset) or even improved (increased by roughly 9 % for the dual-polarimetric dataset).

Constructing and validating readability models: the method of integrating multilevel linguistic features with machine learning.

PubMed

Sung, Yao-Ting; Chen, Ju-Ling; Cha, Ji-Her; Tseng, Hou-Chiang; Chang, Tao-Hsing; Chang, Kuo-En

2015-06-01

Multilevel linguistic features have been proposed for discourse analysis, but there have been few applications of multilevel linguistic features to readability models and also few validations of such models. Most traditional readability formulae are based on generalized linear models (GLMs; e.g., discriminant analysis and multiple regression), but these models have to comply with certain statistical assumptions about data properties and include all of the data in formulae construction without pruning the outliers in advance. The use of such readability formulae tends to produce a low text classification accuracy, while using a support vector machine (SVM) in machine learning can enhance the classification outcome. The present study constructed readability models by integrating multilevel linguistic features with SVM, which is more appropriate for text classification. Taking the Chinese language as an example, this study developed 31 linguistic features as the predicting variables at the word, semantic, syntax, and cohesion levels, with grade levels of texts as the criterion variable. The study compared four types of readability models by integrating unilevel and multilevel linguistic features with GLMs and an SVM. The results indicate that adopting a multilevel approach in readability analysis provides a better representation of the complexities of both texts and the reading comprehension process.

Predicting human liver microsomal stability with machine learning techniques.

PubMed

Sakiyama, Yojiro; Yuki, Hitomi; Moriya, Takashi; Hattori, Kazunari; Suzuki, Misaki; Shimada, Kaoru; Honma, Teruki

2008-02-01

To ensure a continuing pipeline in pharmaceutical research, lead candidates must possess appropriate metabolic stability in the drug discovery process. In vitro ADMET (absorption, distribution, metabolism, elimination, and toxicity) screening provides us with useful information regarding the metabolic stability of compounds. However, before the synthesis stage, an efficient process is required in order to deal with the vast quantity of data from large compound libraries and high-throughput screening. Here we have derived a relationship between the chemical structure and its metabolic stability for a data set of in-house compounds by means of various in silico machine learning such as random forest, support vector machine (SVM), logistic regression, and recursive partitioning. For model building, 1952 proprietary compounds comprising two classes (stable/unstable) were used with 193 descriptors calculated by Molecular Operating Environment. The results using test compounds have demonstrated that all classifiers yielded satisfactory results (accuracy > 0.8, sensitivity > 0.9, specificity > 0.6, and precision > 0.8). Above all, classification by random forest as well as SVM yielded kappa values of approximately 0.7 in an independent validation set, slightly higher than other classification tools. These results suggest that nonlinear/ensemble-based classification methods might prove useful in the area of in silico ADME modeling.

An evaluation of scanpath-comparison and machine-learning classification algorithms used to study the dynamics of analogy making.

PubMed

French, Robert M; Glady, Yannick; Thibaut, Jean-Pierre

2017-08-01

In recent years, eyetracking has begun to be used to study the dynamics of analogy making. Numerous scanpath-comparison algorithms and machine-learning techniques are available that can be applied to the raw eyetracking data. We show how scanpath-comparison algorithms, combined with multidimensional scaling and a classification algorithm, can be used to resolve an outstanding question in analogy making-namely, whether or not children's and adults' strategies in solving analogy problems are different. (They are.) We show which of these scanpath-comparison algorithms is best suited to the kinds of analogy problems that have formed the basis of much analogy-making research over the years. Furthermore, we use machine-learning classification algorithms to examine the item-to-item saccade vectors making up these scanpaths. We show which of these algorithms best predicts, from very early on in a trial, on the basis of the frequency of various item-to-item saccades, whether a child or an adult is doing the problem. This type of analysis can also be used to predict, on the basis of the item-to-item saccade dynamics in the first third of a trial, whether or not a problem will be solved correctly.

Classification of LIDAR Data for Generating a High-Precision Roadway Map

NASA Astrophysics Data System (ADS)

Jeong, J.; Lee, I.

2016-06-01

Generating of a highly precise map grows up with development of autonomous driving vehicles. The highly precise map includes a precision of centimetres level unlike an existing commercial map with the precision of meters level. It is important to understand road environments and make a decision for autonomous driving since a robust localization is one of the critical challenges for the autonomous driving car. The one of source data is from a Lidar because it provides highly dense point cloud data with three dimensional position, intensities and ranges from the sensor to target. In this paper, we focus on how to segment point cloud data from a Lidar on a vehicle and classify objects on the road for the highly precise map. In particular, we propose the combination with a feature descriptor and a classification algorithm in machine learning. Objects can be distinguish by geometrical features based on a surface normal of each point. To achieve correct classification using limited point cloud data sets, a Support Vector Machine algorithm in machine learning are used. Final step is to evaluate accuracies of obtained results by comparing them to reference data The results show sufficient accuracy and it will be utilized to generate a highly precise road map.

Support vector machine and principal component analysis for microarray data classification

NASA Astrophysics Data System (ADS)

Astuti, Widi; Adiwijaya

2018-03-01

Cancer is a leading cause of death worldwide although a significant proportion of it can be cured if it is detected early. In recent decades, technology called microarray takes an important role in the diagnosis of cancer. By using data mining technique, microarray data classification can be performed to improve the accuracy of cancer diagnosis compared to traditional techniques. The characteristic of microarray data is small sample but it has huge dimension. Since that, there is a challenge for researcher to provide solutions for microarray data classification with high performance in both accuracy and running time. This research proposed the usage of Principal Component Analysis (PCA) as a dimension reduction method along with Support Vector Method (SVM) optimized by kernel functions as a classifier for microarray data classification. The proposed scheme was applied on seven data sets using 5-fold cross validation and then evaluation and analysis conducted on term of both accuracy and running time. The result showed that the scheme can obtained 100% accuracy for Ovarian and Lung Cancer data when Linear and Cubic kernel functions are used. In term of running time, PCA greatly reduced the running time for every data sets.

Classification of Partial Discharge Signals by Combining Adaptive Local Iterative Filtering and Entropy Features

PubMed Central

Morison, Gordon; Boreham, Philip

2018-01-01

Electromagnetic Interference (EMI) is a technique for capturing Partial Discharge (PD) signals in High-Voltage (HV) power plant apparatus. EMI signals can be non-stationary which makes their analysis difficult, particularly for pattern recognition applications. This paper elaborates upon a previously developed software condition-monitoring model for improved EMI events classification based on time-frequency signal decomposition and entropy features. The idea of the proposed method is to map multiple discharge source signals captured by EMI and labelled by experts, including PD, from the time domain to a feature space, which aids in the interpretation of subsequent fault information. Here, instead of using only one permutation entropy measure, a more robust measure, called Dispersion Entropy (DE), is added to the feature vector. Multi-Class Support Vector Machine (MCSVM) methods are utilized for classification of the different discharge sources. Results show an improved classification accuracy compared to previously proposed methods. This yields to a successful development of an expert’s knowledge-based intelligent system. Since this method is demonstrated to be successful with real field data, it brings the benefit of possible real-world application for EMI condition monitoring. PMID:29385030

Encoding the local connectivity patterns of fMRI for cognitive task and state classification.

PubMed

Onal Ertugrul, Itir; Ozay, Mete; Yarman Vural, Fatos T

2018-06-15

In this work, we propose a novel framework to encode the local connectivity patterns of brain, using Fisher vectors (FV), vector of locally aggregated descriptors (VLAD) and bag-of-words (BoW) methods. We first obtain local descriptors, called mesh arc descriptors (MADs) from fMRI data, by forming local meshes around anatomical regions, and estimating their relationship within a neighborhood. Then, we extract a dictionary of relationships, called brain connectivity dictionary by fitting a generative Gaussian mixture model (GMM) to a set of MADs, and selecting codewords at the mean of each component of the mixture. Codewords represent connectivity patterns among anatomical regions. We also encode MADs by VLAD and BoW methods using k-Means clustering. We classify cognitive tasks using the Human Connectome Project (HCP) task fMRI dataset and cognitive states using the Emotional Memory Retrieval (EMR). We train support vector machines (SVMs) using the encoded MADs. Results demonstrate that, FV encoding of MADs can be successfully employed for classification of cognitive tasks, and outperform VLAD and BoW representations. Moreover, we identify the significant Gaussians in mixture models by computing energy of their corresponding FV parts, and analyze their effect on classification accuracy. Finally, we suggest a new method to visualize the codewords of the learned brain connectivity dictionary.

Accelerometer and Camera-Based Strategy for Improved Human Fall Detection.

PubMed

Zerrouki, Nabil; Harrou, Fouzi; Sun, Ying; Houacine, Amrane

2016-12-01

In this paper, we address the problem of detecting human falls using anomaly detection. Detection and classification of falls are based on accelerometric data and variations in human silhouette shape. First, we use the exponentially weighted moving average (EWMA) monitoring scheme to detect a potential fall in the accelerometric data. We used an EWMA to identify features that correspond with a particular type of fall allowing us to classify falls. Only features corresponding with detected falls were used in the classification phase. A benefit of using a subset of the original data to design classification models minimizes training time and simplifies models. Based on features corresponding to detected falls, we used the support vector machine (SVM) algorithm to distinguish between true falls and fall-like events. We apply this strategy to the publicly available fall detection databases from the university of Rzeszow's. Results indicated that our strategy accurately detected and classified fall events, suggesting its potential application to early alert mechanisms in the event of fall situations and its capability for classification of detected falls. Comparison of the classification results using the EWMA-based SVM classifier method with those achieved using three commonly used machine learning classifiers, neural network, K-nearest neighbor and naïve Bayes, proved our model superior.

Automated Tissue Classification Framework for Reproducible Chronic Wound Assessment

PubMed Central

Mukherjee, Rashmi; Manohar, Dhiraj Dhane; Das, Dev Kumar; Achar, Arun; Mitra, Analava; Chakraborty, Chandan

2014-01-01

The aim of this paper was to develop a computer assisted tissue classification (granulation, necrotic, and slough) scheme for chronic wound (CW) evaluation using medical image processing and statistical machine learning techniques. The red-green-blue (RGB) wound images grabbed by normal digital camera were first transformed into HSI (hue, saturation, and intensity) color space and subsequently the “S” component of HSI color channels was selected as it provided higher contrast. Wound areas from 6 different types of CW were segmented from whole images using fuzzy divergence based thresholding by minimizing edge ambiguity. A set of color and textural features describing granulation, necrotic, and slough tissues in the segmented wound area were extracted using various mathematical techniques. Finally, statistical learning algorithms, namely, Bayesian classification and support vector machine (SVM), were trained and tested for wound tissue classification in different CW images. The performance of the wound area segmentation protocol was further validated by ground truth images labeled by clinical experts. It was observed that SVM with 3rd order polynomial kernel provided the highest accuracies, that is, 86.94%, 90.47%, and 75.53%, for classifying granulation, slough, and necrotic tissues, respectively. The proposed automated tissue classification technique achieved the highest overall accuracy, that is, 87.61%, with highest kappa statistic value (0.793). PMID:25114925

Automatic classification of schizophrenia using resting-state functional language network via an adaptive learning algorithm

NASA Astrophysics Data System (ADS)

Zhu, Maohu; Jie, Nanfeng; Jiang, Tianzi

2014-03-01

A reliable and precise classification of schizophrenia is significant for its diagnosis and treatment of schizophrenia. Functional magnetic resonance imaging (fMRI) is a novel tool increasingly used in schizophrenia research. Recent advances in statistical learning theory have led to applying pattern classification algorithms to access the diagnostic value of functional brain networks, discovered from resting state fMRI data. The aim of this study was to propose an adaptive learning algorithm to distinguish schizophrenia patients from normal controls using resting-state functional language network. Furthermore, here the classification of schizophrenia was regarded as a sample selection problem where a sparse subset of samples was chosen from the labeled training set. Using these selected samples, which we call informative vectors, a classifier for the clinic diagnosis of schizophrenia was established. We experimentally demonstrated that the proposed algorithm incorporating resting-state functional language network achieved 83.6% leaveone- out accuracy on resting-state fMRI data of 27 schizophrenia patients and 28 normal controls. In contrast with KNearest- Neighbor (KNN), Support Vector Machine (SVM) and l1-norm, our method yielded better classification performance. Moreover, our results suggested that a dysfunction of resting-state functional language network plays an important role in the clinic diagnosis of schizophrenia.

Machine Learning Approach to Automated Quality Identification of Human Induced Pluripotent Stem Cell Colony Images.

PubMed

Joutsijoki, Henry; Haponen, Markus; Rasku, Jyrki; Aalto-Setälä, Katriina; Juhola, Martti

2016-01-01

The focus of this research is on automated identification of the quality of human induced pluripotent stem cell (iPSC) colony images. iPS cell technology is a contemporary method by which the patient's cells are reprogrammed back to stem cells and are differentiated to any cell type wanted. iPS cell technology will be used in future to patient specific drug screening, disease modeling, and tissue repairing, for instance. However, there are technical challenges before iPS cell technology can be used in practice and one of them is quality control of growing iPSC colonies which is currently done manually but is unfeasible solution in large-scale cultures. The monitoring problem returns to image analysis and classification problem. In this paper, we tackle this problem using machine learning methods such as multiclass Support Vector Machines and several baseline methods together with Scaled Invariant Feature Transformation based features. We perform over 80 test arrangements and do a thorough parameter value search. The best accuracy (62.4%) for classification was obtained by using a k-NN classifier showing improved accuracy compared to earlier studies.

Propensity score estimation: machine learning and classification methods as alternatives to logistic regression

PubMed Central

Westreich, Daniel; Lessler, Justin; Funk, Michele Jonsson

2010-01-01

Summary Objective Propensity scores for the analysis of observational data are typically estimated using logistic regression. Our objective in this Review was to assess machine learning alternatives to logistic regression which may accomplish the same goals but with fewer assumptions or greater accuracy. Study Design and Setting We identified alternative methods for propensity score estimation and/or classification from the public health, biostatistics, discrete mathematics, and computer science literature, and evaluated these algorithms for applicability to the problem of propensity score estimation, potential advantages over logistic regression, and ease of use. Results We identified four techniques as alternatives to logistic regression: neural networks, support vector machines, decision trees (CART), and meta-classifiers (in particular, boosting). Conclusion While the assumptions of logistic regression are well understood, those assumptions are frequently ignored. All four alternatives have advantages and disadvantages compared with logistic regression. Boosting (meta-classifiers) and to a lesser extent decision trees (particularly CART) appear to be most promising for use in the context of propensity score analysis, but extensive simulation studies are needed to establish their utility in practice. PMID:20630332

Incremental classification learning for anomaly detection in medical images

NASA Astrophysics Data System (ADS)

Giritharan, Balathasan; Yuan, Xiaohui; Liu, Jianguo

2009-02-01

Computer-aided diagnosis usually screens thousands of instances to find only a few positive cases that indicate probable presence of disease.The amount of patient data increases consistently all the time. In diagnosis of new instances, disagreement occurs between a CAD system and physicians, which suggests inaccurate classifiers. Intuitively, misclassified instances and the previously acquired data should be used to retrain the classifier. This, however, is very time consuming and, in some cases where dataset is too large, becomes infeasible. In addition, among the patient data, only a small percentile shows positive sign, which is known as imbalanced data.We present an incremental Support Vector Machines(SVM) as a solution for the class imbalance problem in classification of anomaly in medical images. The support vectors provide a concise representation of the distribution of the training data. Here we use bootstrapping to identify potential candidate support vectors for future iterations. Experiments were conducted using images from endoscopy videos, and the sensitivity and specificity were close to that of SVM trained using all samples available at a given incremental step with significantly improved efficiency in training the classifier.

Brain tumor classification using the diffusion tensor image segmentation (D-SEG) technique.

PubMed

Jones, Timothy L; Byrnes, Tiernan J; Yang, Guang; Howe, Franklyn A; Bell, B Anthony; Barrick, Thomas R

2015-03-01

There is an increasing demand for noninvasive brain tumor biomarkers to guide surgery and subsequent oncotherapy. We present a novel whole-brain diffusion tensor imaging (DTI) segmentation (D-SEG) to delineate tumor volumes of interest (VOIs) for subsequent classification of tumor type. D-SEG uses isotropic (p) and anisotropic (q) components of the diffusion tensor to segment regions with similar diffusion characteristics. DTI scans were acquired from 95 patients with low- and high-grade glioma, metastases, and meningioma and from 29 healthy subjects. D-SEG uses k-means clustering of the 2D (p,q) space to generate segments with different isotropic and anisotropic diffusion characteristics. Our results are visualized using a novel RGB color scheme incorporating p, q and T2-weighted information within each segment. The volumetric contribution of each segment to gray matter, white matter, and cerebrospinal fluid spaces was used to generate healthy tissue D-SEG spectra. Tumor VOIs were extracted using a semiautomated flood-filling technique and D-SEG spectra were computed within the VOI. Classification of tumor type using D-SEG spectra was performed using support vector machines. D-SEG was computationally fast and stable and delineated regions of healthy tissue from tumor and edema. D-SEG spectra were consistent for each tumor type, with constituent diffusion characteristics potentially reflecting regional differences in tissue microstructure. Support vector machines classified tumor type with an overall accuracy of 94.7%, providing better classification than previously reported. D-SEG presents a user-friendly, semiautomated biomarker that may provide a valuable adjunct in noninvasive brain tumor diagnosis and treatment planning. © The Author(s) 2014. Published by Oxford University Press on behalf of the Society for Neuro-Oncology.

Incorporation of support vector machines in the LIBS toolbox for sensitive and robust classification amidst unexpected sample and system variability

PubMed Central

ChariDingari, Narahara; Barman, Ishan; Myakalwar, Ashwin Kumar; Tewari, Surya P.; Kumar, G. Manoj

2012-01-01

Despite the intrinsic elemental analysis capability and lack of sample preparation requirements, laser-induced breakdown spectroscopy (LIBS) has not been extensively used for real world applications, e.g. quality assurance and process monitoring. Specifically, variability in sample, system and experimental parameters in LIBS studies present a substantive hurdle for robust classification, even when standard multivariate chemometric techniques are used for analysis. Considering pharmaceutical sample investigation as an example, we propose the use of support vector machines (SVM) as a non-linear classification method over conventional linear techniques such as soft independent modeling of class analogy (SIMCA) and partial least-squares discriminant analysis (PLS-DA) for discrimination based on LIBS measurements. Using over-the-counter pharmaceutical samples, we demonstrate that application of SVM enables statistically significant improvements in prospective classification accuracy (sensitivity), due to its ability to address variability in LIBS sample ablation and plasma self-absorption behavior. Furthermore, our results reveal that SVM provides nearly 10% improvement in correct allocation rate and a concomitant reduction in misclassification rates of 75% (cf. PLS-DA) and 80% (cf. SIMCA)-when measurements from samples not included in the training set are incorporated in the test data – highlighting its robustness. While further studies on a wider matrix of sample types performed using different LIBS systems is needed to fully characterize the capability of SVM to provide superior predictions, we anticipate that the improved sensitivity and robustness observed here will facilitate application of the proposed LIBS-SVM toolbox for screening drugs and detecting counterfeit samples as well as in related areas of forensic and biological sample analysis. PMID:22292496

Ensemble Feature Learning of Genomic Data Using Support Vector Machine

PubMed Central

Anaissi, Ali; Goyal, Madhu; Catchpoole, Daniel R.; Braytee, Ali; Kennedy, Paul J.

2016-01-01

The identification of a subset of genes having the ability to capture the necessary information to distinguish classes of patients is crucial in bioinformatics applications. Ensemble and bagging methods have been shown to work effectively in the process of gene selection and classification. Testament to that is random forest which combines random decision trees with bagging to improve overall feature selection and classification accuracy. Surprisingly, the adoption of these methods in support vector machines has only recently received attention but mostly on classification not gene selection. This paper introduces an ensemble SVM-Recursive Feature Elimination (ESVM-RFE) for gene selection that follows the concepts of ensemble and bagging used in random forest but adopts the backward elimination strategy which is the rationale of RFE algorithm. The rationale behind this is, building ensemble SVM models using randomly drawn bootstrap samples from the training set, will produce different feature rankings which will be subsequently aggregated as one feature ranking. As a result, the decision for elimination of features is based upon the ranking of multiple SVM models instead of choosing one particular model. Moreover, this approach will address the problem of imbalanced datasets by constructing a nearly balanced bootstrap sample. Our experiments show that ESVM-RFE for gene selection substantially increased the classification performance on five microarray datasets compared to state-of-the-art methods. Experiments on the childhood leukaemia dataset show that an average 9% better accuracy is achieved by ESVM-RFE over SVM-RFE, and 5% over random forest based approach. The selected genes by the ESVM-RFE algorithm were further explored with Singular Value Decomposition (SVD) which reveals significant clusters with the selected data. PMID:27304923

Multilevel image recognition using discriminative patches and kernel covariance descriptor

NASA Astrophysics Data System (ADS)

Lu, Le; Yao, Jianhua; Turkbey, Evrim; Summers, Ronald M.

2014-03-01

Computer-aided diagnosis of medical images has emerged as an important tool to objectively improve the performance, accuracy and consistency for clinical workflow. To computerize the medical image diagnostic recognition problem, there are three fundamental problems: where to look (i.e., where is the region of interest from the whole image/volume), image feature description/encoding, and similarity metrics for classification or matching. In this paper, we exploit the motivation, implementation and performance evaluation of task-driven iterative, discriminative image patch mining; covariance matrix based descriptor via intensity, gradient and spatial layout; and log-Euclidean distance kernel for support vector machine, to address these three aspects respectively. To cope with often visually ambiguous image patterns for the region of interest in medical diagnosis, discovery of multilabel selective discriminative patches is desired. Covariance of several image statistics summarizes their second order interactions within an image patch and is proved as an effective image descriptor, with low dimensionality compared with joint statistics and fast computation regardless of the patch size. We extensively evaluate two extended Gaussian kernels using affine-invariant Riemannian metric or log-Euclidean metric with support vector machines (SVM), on two medical image classification problems of degenerative disc disease (DDD) detection on cortical shell unwrapped CT maps and colitis detection on CT key images. The proposed approach is validated with promising quantitative results on these challenging tasks. Our experimental findings and discussion also unveil some interesting insights on the covariance feature composition with or without spatial layout for classification and retrieval, and different kernel constructions for SVM. This will also shed some light on future work using covariance feature and kernel classification for medical image analysis.

Enhancing spatial resolution of (18)F positron imaging with the Timepix detector by classification of primary fired pixels using support vector machine.

PubMed

Wang, Qian; Liu, Zhen; Ziegler, Sibylle I; Shi, Kuangyu

2015-07-07

Position-sensitive positron cameras using silicon pixel detectors have been applied for some preclinical and intraoperative clinical applications. However, the spatial resolution of a positron camera is limited by positron multiple scattering in the detector. An incident positron may fire a number of successive pixels on the imaging plane. It is still impossible to capture the primary fired pixel along a particle trajectory by hardware or to perceive the pixel firing sequence by direct observation. Here, we propose a novel data-driven method to improve the spatial resolution by classifying the primary pixels within the detector using support vector machine. A classification model is constructed by learning the features of positron trajectories based on Monte-Carlo simulations using Geant4. Topological and energy features of pixels fired by (18)F positrons were considered for the training and classification. After applying the classification model on measurements, the primary fired pixels of the positron tracks in the silicon detector were estimated. The method was tested and assessed for [(18)F]FDG imaging of an absorbing edge protocol and a leaf sample. The proposed method improved the spatial resolution from 154.6   ±   4.2 µm (energy weighted centroid approximation) to 132.3   ±   3.5 µm in the absorbing edge measurements. For the positron imaging of a leaf sample, the proposed method achieved lower root mean square error relative to phosphor plate imaging, and higher similarity with the reference optical image. The improvements of the preliminary results support further investigation of the proposed algorithm for the enhancement of positron imaging in clinical and preclinical applications.

Enhancing spatial resolution of 18F positron imaging with the Timepix detector by classification of primary fired pixels using support vector machine

NASA Astrophysics Data System (ADS)

Wang, Qian; Liu, Zhen; Ziegler, Sibylle I.; Shi, Kuangyu

2015-07-01

Position-sensitive positron cameras using silicon pixel detectors have been applied for some preclinical and intraoperative clinical applications. However, the spatial resolution of a positron camera is limited by positron multiple scattering in the detector. An incident positron may fire a number of successive pixels on the imaging plane. It is still impossible to capture the primary fired pixel along a particle trajectory by hardware or to perceive the pixel firing sequence by direct observation. Here, we propose a novel data-driven method to improve the spatial resolution by classifying the primary pixels within the detector using support vector machine. A classification model is constructed by learning the features of positron trajectories based on Monte-Carlo simulations using Geant4. Topological and energy features of pixels fired by 18F positrons were considered for the training and classification. After applying the classification model on measurements, the primary fired pixels of the positron tracks in the silicon detector were estimated. The method was tested and assessed for [18F]FDG imaging of an absorbing edge protocol and a leaf sample. The proposed method improved the spatial resolution from 154.6   ±   4.2 µm (energy weighted centroid approximation) to 132.3   ±   3.5 µm in the absorbing edge measurements. For the positron imaging of a leaf sample, the proposed method achieved lower root mean square error relative to phosphor plate imaging, and higher similarity with the reference optical image. The improvements of the preliminary results support further investigation of the proposed algorithm for the enhancement of positron imaging in clinical and preclinical applications.

Incorporation of support vector machines in the LIBS toolbox for sensitive and robust classification amidst unexpected sample and system variability.

PubMed

Dingari, Narahara Chari; Barman, Ishan; Myakalwar, Ashwin Kumar; Tewari, Surya P; Kumar Gundawar, Manoj

2012-03-20

Despite the intrinsic elemental analysis capability and lack of sample preparation requirements, laser-induced breakdown spectroscopy (LIBS) has not been extensively used for real-world applications, e.g., quality assurance and process monitoring. Specifically, variability in sample, system, and experimental parameters in LIBS studies present a substantive hurdle for robust classification, even when standard multivariate chemometric techniques are used for analysis. Considering pharmaceutical sample investigation as an example, we propose the use of support vector machines (SVM) as a nonlinear classification method over conventional linear techniques such as soft independent modeling of class analogy (SIMCA) and partial least-squares discriminant analysis (PLS-DA) for discrimination based on LIBS measurements. Using over-the-counter pharmaceutical samples, we demonstrate that the application of SVM enables statistically significant improvements in prospective classification accuracy (sensitivity), because of its ability to address variability in LIBS sample ablation and plasma self-absorption behavior. Furthermore, our results reveal that SVM provides nearly 10% improvement in correct allocation rate and a concomitant reduction in misclassification rates of 75% (cf. PLS-DA) and 80% (cf. SIMCA)-when measurements from samples not included in the training set are incorporated in the test data-highlighting its robustness. While further studies on a wider matrix of sample types performed using different LIBS systems is needed to fully characterize the capability of SVM to provide superior predictions, we anticipate that the improved sensitivity and robustness observed here will facilitate application of the proposed LIBS-SVM toolbox for screening drugs and detecting counterfeit samples, as well as in related areas of forensic and biological sample analysis.

Automated classification of optical coherence tomography images of human atrial tissue

NASA Astrophysics Data System (ADS)

Gan, Yu; Tsay, David; Amir, Syed B.; Marboe, Charles C.; Hendon, Christine P.

2016-10-01

Tissue composition of the atria plays a critical role in the pathology of cardiovascular disease, tissue remodeling, and arrhythmogenic substrates. Optical coherence tomography (OCT) has the ability to capture the tissue composition information of the human atria. In this study, we developed a region-based automated method to classify tissue compositions within human atria samples within OCT images. We segmented regional information without prior information about the tissue architecture and subsequently extracted features within each segmented region. A relevance vector machine model was used to perform automated classification. Segmentation of human atrial ex vivo datasets was correlated with trichrome histology and our classification algorithm had an average accuracy of 80.41% for identifying adipose, myocardium, fibrotic myocardium, and collagen tissue compositions.

Advanced Methods for Passive Acoustic Detection, Classification, and Localization of Marine Mammals

DTIC Science & Technology

2012-09-30

floor 1176 Howell St Newport RI 02842 phone: (401) 832-5749 fax: (401) 832-4441 email: David.Moretti@navy.mil Steve W. Martin SPAWAR...multiclass support vector machine (SVM) classifier was previously developed ( Jarvis et al. 2008). This classifier both detects and classifies echolocation...whales. Here Moretti’s group, especially S. Jarvis , will improve the SVM classifier by resolving confusion between species whose clicks overlap in

Generative Models for Similarity-based Classification

DTIC Science & Technology

2007-01-01

NC), local nearest centroid (local NC), k-nearest neighbors ( kNN ), and condensed nearest neighbors (CNN) are all similarity-based classifiers which...vector machine to the k nearest neighbors of the test sample [80]. The SVM- KNN method was developed to address the robustness and dimensionality...concerns that afflict nearest neighbors and SVMs. Similarly to the nearest-means classifier, the SVM- KNN is a hybrid local and global classifier developed

Beam-hardening correction by a surface fitting and phase classification by a least square support vector machine approach for tomography images of geological samples

NASA Astrophysics Data System (ADS)

Khan, F.; Enzmann, F.; Kersten, M.

2015-12-01

In X-ray computed microtomography (μXCT) image processing is the most important operation prior to image analysis. Such processing mainly involves artefact reduction and image segmentation. We propose a new two-stage post-reconstruction procedure of an image of a geological rock core obtained by polychromatic cone-beam μXCT technology. In the first stage, the beam-hardening (BH) is removed applying a best-fit quadratic surface algorithm to a given image data set (reconstructed slice), which minimizes the BH offsets of the attenuation data points from that surface. The final BH-corrected image is extracted from the residual data, or the difference between the surface elevation values and the original grey-scale values. For the second stage, we propose using a least square support vector machine (a non-linear classifier algorithm) to segment the BH-corrected data as a pixel-based multi-classification task. A combination of the two approaches was used to classify a complex multi-mineral rock sample. The Matlab code for this approach is provided in the Appendix. A minor drawback is that the proposed segmentation algorithm may become computationally demanding in the case of a high dimensional training data set.

A Non-Parametric Approach for the Activation Detection of Block Design fMRI Simulated Data Using Self-Organizing Maps and Support Vector Machine.

PubMed

Bahrami, Sheyda; Shamsi, Mousa

2017-01-01

Functional magnetic resonance imaging (fMRI) is a popular method to probe the functional organization of the brain using hemodynamic responses. In this method, volume images of the entire brain are obtained with a very good spatial resolution and low temporal resolution. However, they always suffer from high dimensionality in the face of classification algorithms. In this work, we combine a support vector machine (SVM) with a self-organizing map (SOM) for having a feature-based classification by using SVM. Then, a linear kernel SVM is used for detecting the active areas. Here, we use SOM for feature extracting and labeling the datasets. SOM has two major advances: (i) it reduces dimension of data sets for having less computational complexity and (ii) it is useful for identifying brain regions with small onset differences in hemodynamic responses. Our non-parametric model is compared with parametric and non-parametric methods. We use simulated fMRI data sets and block design inputs in this paper and consider the contrast to noise ratio (CNR) value equal to 0.6 for simulated datasets. fMRI simulated dataset has contrast 1-4% in active areas. The accuracy of our proposed method is 93.63% and the error rate is 6.37%.