Science.gov

Sample records for achieved classification accuracies

  1. Classification accuracy improvement

    NASA Technical Reports Server (NTRS)

    Kistler, R.; Kriegler, F. J.

    1977-01-01

    Improvements made in processing system designed for MIDAS (prototype multivariate interactive digital analysis system) effects higher accuracy in classification of pixels, resulting in significantly-reduced processing time. Improved system realizes cost reduction factor of 20 or more.

  2. Linear Discriminant Analysis Achieves High Classification Accuracy for the BOLD fMRI Response to Naturalistic Movie Stimuli.

    PubMed

    Mandelkow, Hendrik; de Zwart, Jacco A; Duyn, Jeff H

    2016-01-01

    Naturalistic stimuli like movies evoke complex perceptual processes, which are of great interest in the study of human cognition by functional MRI (fMRI). However, conventional fMRI analysis based on statistical parametric mapping (SPM) and the general linear model (GLM) is hampered by a lack of accurate parametric models of the BOLD response to complex stimuli. In this situation, statistical machine-learning methods, a.k.a. multivariate pattern analysis (MVPA), have received growing attention for their ability to generate stimulus response models in a data-driven fashion. However, machine-learning methods typically require large amounts of training data as well as computational resources. In the past, this has largely limited their application to fMRI experiments involving small sets of stimulus categories and small regions of interest in the brain. By contrast, the present study compares several classification algorithms known as Nearest Neighbor (NN), Gaussian Naïve Bayes (GNB), and (regularized) Linear Discriminant Analysis (LDA) in terms of their classification accuracy in discriminating the global fMRI response patterns evoked by a large number of naturalistic visual stimuli presented as a movie. Results show that LDA regularized by principal component analysis (PCA) achieved high classification accuracies, above 90% on average for single fMRI volumes acquired 2 s apart during a 300 s movie (chance level 0.7% = 2 s/300 s). The largest source of classification errors were autocorrelations in the BOLD signal compounded by the similarity of consecutive stimuli. All classifiers performed best when given input features from a large region of interest comprising around 25% of the voxels that responded significantly to the visual stimulus. Consistent with this, the most informative principal components represented widespread distributions of co-activated brain regions that were similar between subjects and may represent functional networks. In light of these

  3. Linear Discriminant Analysis Achieves High Classification Accuracy for the BOLD fMRI Response to Naturalistic Movie Stimuli.

    PubMed

    Mandelkow, Hendrik; de Zwart, Jacco A; Duyn, Jeff H

    2016-01-01

    Naturalistic stimuli like movies evoke complex perceptual processes, which are of great interest in the study of human cognition by functional MRI (fMRI). However, conventional fMRI analysis based on statistical parametric mapping (SPM) and the general linear model (GLM) is hampered by a lack of accurate parametric models of the BOLD response to complex stimuli. In this situation, statistical machine-learning methods, a.k.a. multivariate pattern analysis (MVPA), have received growing attention for their ability to generate stimulus response models in a data-driven fashion. However, machine-learning methods typically require large amounts of training data as well as computational resources. In the past, this has largely limited their application to fMRI experiments involving small sets of stimulus categories and small regions of interest in the brain. By contrast, the present study compares several classification algorithms known as Nearest Neighbor (NN), Gaussian Naïve Bayes (GNB), and (regularized) Linear Discriminant Analysis (LDA) in terms of their classification accuracy in discriminating the global fMRI response patterns evoked by a large number of naturalistic visual stimuli presented as a movie. Results show that LDA regularized by principal component analysis (PCA) achieved high classification accuracies, above 90% on average for single fMRI volumes acquired 2 s apart during a 300 s movie (chance level 0.7% = 2 s/300 s). The largest source of classification errors were autocorrelations in the BOLD signal compounded by the similarity of consecutive stimuli. All classifiers performed best when given input features from a large region of interest comprising around 25% of the voxels that responded significantly to the visual stimulus. Consistent with this, the most informative principal components represented widespread distributions of co-activated brain regions that were similar between subjects and may represent functional networks. In light of these

  4. Linear Discriminant Analysis Achieves High Classification Accuracy for the BOLD fMRI Response to Naturalistic Movie Stimuli

    PubMed Central

    Mandelkow, Hendrik; de Zwart, Jacco A.; Duyn, Jeff H.

    2016-01-01

    Naturalistic stimuli like movies evoke complex perceptual processes, which are of great interest in the study of human cognition by functional MRI (fMRI). However, conventional fMRI analysis based on statistical parametric mapping (SPM) and the general linear model (GLM) is hampered by a lack of accurate parametric models of the BOLD response to complex stimuli. In this situation, statistical machine-learning methods, a.k.a. multivariate pattern analysis (MVPA), have received growing attention for their ability to generate stimulus response models in a data-driven fashion. However, machine-learning methods typically require large amounts of training data as well as computational resources. In the past, this has largely limited their application to fMRI experiments involving small sets of stimulus categories and small regions of interest in the brain. By contrast, the present study compares several classification algorithms known as Nearest Neighbor (NN), Gaussian Naïve Bayes (GNB), and (regularized) Linear Discriminant Analysis (LDA) in terms of their classification accuracy in discriminating the global fMRI response patterns evoked by a large number of naturalistic visual stimuli presented as a movie. Results show that LDA regularized by principal component analysis (PCA) achieved high classification accuracies, above 90% on average for single fMRI volumes acquired 2 s apart during a 300 s movie (chance level 0.7% = 2 s/300 s). The largest source of classification errors were autocorrelations in the BOLD signal compounded by the similarity of consecutive stimuli. All classifiers performed best when given input features from a large region of interest comprising around 25% of the voxels that responded significantly to the visual stimulus. Consistent with this, the most informative principal components represented widespread distributions of co-activated brain regions that were similar between subjects and may represent functional networks. In light of these

  5. Landsat classification accuracy assessment procedures

    USGS Publications Warehouse

    Mead, R. R.; Szajgin, John

    1982-01-01

    A working conference was held in Sioux Falls, South Dakota, 12-14 November, 1980 dealing with Landsat classification Accuracy Assessment Procedures. Thirteen formal presentations were made on three general topics: (1) sampling procedures, (2) statistical analysis techniques, and (3) examples of projects which included accuracy assessment and the associated costs, logistical problems, and value of the accuracy data to the remote sensing specialist and the resource manager. Nearly twenty conference attendees participated in two discussion sessions addressing various issues associated with accuracy assessment. This paper presents an account of the accomplishments of the conference.

  6. Improving Accuracy of Image Classification Using GIS

    NASA Astrophysics Data System (ADS)

    Gupta, R. K.; Prasad, T. S.; Bala Manikavelu, P. M.; Vijayan, D.

    The Remote Sensing signal which reaches sensor on-board the satellite is the complex aggregation of signals (in agriculture field for example) from soil (with all its variations such as colour, texture, particle size, clay content, organic and nutrition content, inorganic content, water content etc.), plant (height, architecture, leaf area index, mean canopy inclination etc.), canopy closure status and atmospheric effects, and from this we want to find say, characteristics of vegetation. If sensor on- board the satellite makes measurements in n-bands (n of n*1 dimension) and number of classes in an image are c (f of c*1 dimension), then considering linear mixture modeling the pixel classification problem could be written as n = m* f +, where m is the transformation matrix of (n*c) dimension and therepresents the error vector (noise). The problem is to estimate f by inverting the above equation and the possible solutions for such problem are many. Thus, getting back individual classes from satellite data is an ill-posed inverse problem for which unique solution is not feasible and this puts limit to the obtainable classification accuracy. Maximum Likelihood (ML) is the constraint mostly practiced in solving such a situation which suffers from the handicaps of assumed Gaussian distribution and random nature of pixels (in-fact there is high auto-correlation among the pixels of a specific class and further high auto-correlation among the pixels in sub- classes where the homogeneity would be high among pixels). Due to this, achieving of very high accuracy in the classification of remote sensing images is not a straight proposition. With the availability of the GIS for the area under study (i) a priori probability for different classes could be assigned to ML classifier in more realistic terms and (ii) the purity of training sets for different thematic classes could be better ascertained. To what extent this could improve the accuracy of classification in ML classifier

  7. A Nonparametric Approach to Estimate Classification Accuracy and Consistency

    ERIC Educational Resources Information Center

    Lathrop, Quinn N.; Cheng, Ying

    2014-01-01

    When cut scores for classifications occur on the total score scale, popular methods for estimating classification accuracy (CA) and classification consistency (CC) require assumptions about a parametric form of the test scores or about a parametric response model, such as item response theory (IRT). This article develops an approach to estimate CA…

  8. Estimating Classification Consistency and Accuracy for Cognitive Diagnostic Assessment

    ERIC Educational Resources Information Center

    Cui, Ying; Gierl, Mark J.; Chang, Hua-Hua

    2012-01-01

    This article introduces procedures for the computation and asymptotic statistical inference for classification consistency and accuracy indices specifically designed for cognitive diagnostic assessments. The new classification indices can be used as important indicators of the reliability and validity of classification results produced by…

  9. Nationwide forestry applications program. Analysis of forest classification accuracy

    NASA Technical Reports Server (NTRS)

    Congalton, R. G.; Mead, R. A.; Oderwald, R. G.; Heinen, J. (Principal Investigator)

    1981-01-01

    The development of LANDSAT classification accuracy assessment techniques, and of a computerized system for assessing wildlife habitat from land cover maps are considered. A literature review on accuracy assessment techniques and an explanation for the techniques development under both projects are included along with listings of the computer programs. The presentations and discussions at the National Working Conference on LANDSAT Classification Accuracy are summarized. Two symposium papers which were published on the results of this project are appended.

  10. Achieving Climate Change Absolute Accuracy in Orbit

    NASA Technical Reports Server (NTRS)

    Wielicki, Bruce A.; Young, D. F.; Mlynczak, M. G.; Thome, K. J; Leroy, S.; Corliss, J.; Anderson, J. G.; Ao, C. O.; Bantges, R.; Best, F.; Bowman, K.; Brindley, H.; Butler, J. J.; Collins, W.; Dykema, J. A.; Doelling, D. R.; Feldman, D. R.; Fox, N.; Huang, X.; Holz, R.; Huang, Y.; Jennings, D.; Jin, Z.; Johnson, D. G.; Jucks, K.; Kato, S.; Kratz, D. P.; Liu, X.; Lukashin, C.; Mannucci, A. J.; Phojanamongkolkij, N.; Roithmayr, C. M.; Sandford, S.; Taylor, P. C.; Xiong, X.

    2013-01-01

    The Climate Absolute Radiance and Refractivity Observatory (CLARREO) mission will provide a calibration laboratory in orbit for the purpose of accurately measuring and attributing climate change. CLARREO measurements establish new climate change benchmarks with high absolute radiometric accuracy and high statistical confidence across a wide range of essential climate variables. CLARREO's inherently high absolute accuracy will be verified and traceable on orbit to Système Internationale (SI) units. The benchmarks established by CLARREO will be critical for assessing changes in the Earth system and climate model predictive capabilities for decades into the future as society works to meet the challenge of optimizing strategies for mitigating and adapting to climate change. The CLARREO benchmarks are derived from measurements of the Earth's thermal infrared spectrum (5-50 micron), the spectrum of solar radiation reflected by the Earth and its atmosphere (320-2300 nm), and radio occultation refractivity from which accurate temperature profiles are derived. The mission has the ability to provide new spectral fingerprints of climate change, as well as to provide the first orbiting radiometer with accuracy sufficient to serve as the reference transfer standard for other space sensors, in essence serving as a "NIST [National Institute of Standards and Technology] in orbit." CLARREO will greatly improve the accuracy and relevance of a wide range of space-borne instruments for decadal climate change. Finally, CLARREO has developed new metrics and methods for determining the accuracy requirements of climate observations for a wide range of climate variables and uncertainty sources. These methods should be useful for improving our understanding of observing requirements for most climate change observations.

  11. Classification, change-detection and accuracy assessment: Toward fuller automation

    NASA Astrophysics Data System (ADS)

    Podger, Nancy E.

    This research aims to automate methods for conducting change detection studies using remotely sensed images. Five major objectives were tested on two study sites, one encompassing Madison, Wisconsin, and the other Fort Hood, Texas. (Objective 1) Enhance accuracy assessments by estimating standard errors using bootstrap analysis. Bootstrap estimates of the standard errors were found to be comparable to parametric statistical estimates. Also, results show that bootstrapping can be used to evaluate the consistency of a classification process. (Objective 2) Automate the guided clustering classifier. This research shows that the guided clustering classification process can be automated while maintaining highly accurate results. Three different evaluation methods were used. (Evaluation 1) Appraised the consistency of 25 classifications produced from the automated system. The classifications differed from one another by only two to four percent. (Evaluation 2) Compared accuracies produced by the automated system to classification accuracies generated following a manual guided clustering protocol. Results: The automated system produced higher overall accuracies in 50 percent of the tests and was comparable for all but one of the remaining tests. (Evaluation 3) Assessed the time and effort required to produce accurate classifications. Results: The automated system produced classifications in less time and with less effort than the manual 'protocol' method. (Objective 3) Built a flexible, interactive software tool to aid in producing binary change masks. (Objective 4) Reduced by automation the amount of training data needed to classify the second image of a two-time-period change detection project. Locations of the training sites in 'unchanged' areas employed to classify the first image were used to identify sites where spectral information was automatically extracted from the second image. Results: The automatically generated training data produces classification accuracies

  12. Use of collateral information to improve LANDSAT classification accuracies

    NASA Technical Reports Server (NTRS)

    Strahler, A. H. (Principal Investigator)

    1981-01-01

    Methods to improve LANDSAT classification accuracies were investigated including: (1) the use of prior probabilities in maximum likelihood classification as a methodology to integrate discrete collateral data with continuously measured image density variables; (2) the use of the logit classifier as an alternative to multivariate normal classification that permits mixing both continuous and categorical variables in a single model and fits empirical distributions of observations more closely than the multivariate normal density function; and (3) the use of collateral data in a geographic information system as exercised to model a desired output information layer as a function of input layers of raster format collateral and image data base layers.

  13. 100% Classification Accuracy Considered Harmful: The Normalized Information Transfer Factor Explains the Accuracy Paradox

    PubMed Central

    Valverde-Albacete, Francisco J.; Peláez-Moreno, Carmen

    2014-01-01

    The most widely spread measure of performance, accuracy, suffers from a paradox: predictive models with a given level of accuracy may have greater predictive power than models with higher accuracy. Despite optimizing classification error rate, high accuracy models may fail to capture crucial information transfer in the classification task. We present evidence of this behavior by means of a combinatorial analysis where every possible contingency matrix of 2, 3 and 4 classes classifiers are depicted on the entropy triangle, a more reliable information-theoretic tool for classification assessment. Motivated by this, we develop from first principles a measure of classification performance that takes into consideration the information learned by classifiers. We are then able to obtain the entropy-modulated accuracy (EMA), a pessimistic estimate of the expected accuracy with the influence of the input distribution factored out, and the normalized information transfer factor (NIT), a measure of how efficient is the transmission of information from the input to the output set of classes. The EMA is a more natural measure of classification performance than accuracy when the heuristic to maximize is the transfer of information through the classifier instead of classification error count. The NIT factor measures the effectiveness of the learning process in classifiers and also makes it harder for them to “cheat” using techniques like specialization, while also promoting the interpretability of results. Their use is demonstrated in a mind reading task competition that aims at decoding the identity of a video stimulus based on magnetoencephalography recordings. We show how the EMA and the NIT factor reject rankings based in accuracy, choosing more meaningful and interpretable classifiers. PMID:24427282

  14. An Evaluation of Item Response Theory Classification Accuracy and Consistency Indices

    ERIC Educational Resources Information Center

    Wyse, Adam E.; Hao, Shiqi

    2012-01-01

    This article introduces two new classification consistency indices that can be used when item response theory (IRT) models have been applied. The new indices are shown to be related to Rudner's classification accuracy index and Guo's classification accuracy index. The Rudner- and Guo-based classification accuracy and consistency indices are…

  15. Impact of spatial resolution on correlation between segmentation evaluation metrics and forest classification accuracy

    NASA Astrophysics Data System (ADS)

    Švab Lenarčič, Andreja; Ritlop, Klemen; Äńurić, Nataša.; Čotar, Klemen; Oštir, Krištof

    2015-10-01

    Slovenia is one of the most forested countries in Europe. Its forest management authorities need information about the forest extent and state, as their responsibility lies in forest observation and preservation. Together with appropriate geographic information system mapping methods the remotely sensed data represent essential tool for an effective and sustainable forest management. Despite the large data availability, suitable mapping methods still present big challenge in terms of their speed which is often affected by the huge amount of data. The speed of the classification method could be maximised, if each of the steps in object-based classification was automated. However, automation is hard to achieve, since segmentation requires choosing optimum parameter values for optimal classification results. This paper focuses on the analysis of segmentation and classification performance and their correlation in a range of segmentation parameter values applied in the segmentation step. In order to find out which spatial resolution is still suitable for forest classification, forest classification accuracies obtained by using four images with different spatial resolutions were compared. Results of this study indicate that all high or very high spatial resolutions are suitable for optimal forest segmentation and classification, as long as appropriate scale and merge parameters combinations are used in the object-based classification. If computation interval includes all segmentation parameter combinations, all segmentation-classification correlations are spatial resolution independent and are generally high. If computation interval includes over- or optimal-segmentation parameter combinations, most segmentation-classification correlations are spatial resolution dependent.

  16. Does feature selection improve classification accuracy? Impact of sample size and feature selection on classification using anatomical magnetic resonance images.

    PubMed

    Chu, Carlton; Hsu, Ai-Ling; Chou, Kun-Hsien; Bandettini, Peter; Lin, Chingpo

    2012-03-01

    There are growing numbers of studies using machine learning approaches to characterize patterns of anatomical difference discernible from neuroimaging data. The high-dimensionality of image data often raises a concern that feature selection is needed to obtain optimal accuracy. Among previous studies, mostly using fixed sample sizes, some show greater predictive accuracies with feature selection, whereas others do not. In this study, we compared four common feature selection methods. 1) Pre-selected region of interests (ROIs) that are based on prior knowledge. 2) Univariate t-test filtering. 3) Recursive feature elimination (RFE), and 4) t-test filtering constrained by ROIs. The predictive accuracies achieved from different sample sizes, with and without feature selection, were compared statistically. To demonstrate the effect, we used grey matter segmented from the T1-weighted anatomical scans collected by the Alzheimer's disease Neuroimaging Initiative (ADNI) as the input features to a linear support vector machine classifier. The objective was to characterize the patterns of difference between Alzheimer's disease (AD) patients and cognitively normal subjects, and also to characterize the difference between mild cognitive impairment (MCI) patients and normal subjects. In addition, we also compared the classification accuracies between MCI patients who converted to AD and MCI patients who did not convert within the period of 12 months. Predictive accuracies from two data-driven feature selection methods (t-test filtering and RFE) were no better than those achieved using whole brain data. We showed that we could achieve the most accurate characterizations by using prior knowledge of where to expect neurodegeneration (hippocampus and parahippocampal gyrus). Therefore, feature selection does improve the classification accuracies, but it depends on the method adopted. In general, larger sample sizes yielded higher accuracies with less advantage obtained by using

  17. Enhancing accuracy of mental fatigue classification using advanced computational intelligence in an electroencephalography system.

    PubMed

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

    2014-01-01

    A system using electroencephalography (EEG) signals could enhance the detection of mental fatigue while driving a vehicle. This paper examines the classification between fatigue and alert states using an autoregressive (AR) model-based power spectral density (PSD) as the features extraction method and fuzzy particle swarm optimization with cross mutated of artificial neural network (FPSOCM-ANN) as the classification method. Using 32-EEG channels, results indicated an improved overall specificity from 76.99% to 82.02%, an improved sensitivity from 74.92 to 78.99% and an improved accuracy from 75.95% to 80.51% when compared to previous studies. The classification using fewer EEG channels, with eleven frontal sites resulted in 77.52% for specificity, 73.78% for sensitivity and 75.65% accuracy being achieved. For ergonomic reasons, the configuration with fewer EEG channels will enhance capacity to monitor fatigue as there is less set-up time required. PMID:25570210

  18. A study of neural network parameters for improvement in classification accuracy

    NASA Astrophysics Data System (ADS)

    Pathak, Avijit; Tiwari, K. C.

    2016-05-01

    Hyperspectral data due to large number of spectral bands facilitates discrimination between large numbers of classes in a data; however, the advantage afforded by the hyperspectral data often tends to get lost in the limitations of convection al classifier techniques. Artificial Neural Networks (ANN) in several studies has shown to outperform convection al classifiers, however; there are several issues with regard to selection of parameters for achieving best possible classification accuracy. Objectives of this study have been accordingly formulated to include an investigation of t he effect of various Neural Network parameters on the accuracy of hyperspectral image classification. AVIRIS Hyperspectral Indian Pine Test site 3 dataset acquiredin220 Bands on June 12, 1992 has been used in the stud y. Thereafter, maximal feature extraction technique of Principle component analysis (PCA) is used to reduce the dataset t o 10 bands preserving of 99.96% variance. The data contains 16 major classes of which 4 have been considered for ANN based classification. The parameters selected for the study are - number of hidden layers, hidden Nodes, training sample size, learning rate and learning momentum. Backpropagation method of learning is adopted. The overall accuracy of the network trained has been assessed using test sample size of 300 pixels. Although, the study throws up certain distinct ranges within which higher classification accuracies can be expected, however, no definite relationship could be identified between various ANN parameters under study.

  19. 3D imaging: how to achieve highest accuracy

    NASA Astrophysics Data System (ADS)

    Luhmann, Thomas

    2011-07-01

    The generation of 3D information from images is a key technology in many different areas, e.g. in 3D modeling and representation of architectural or heritage objects, in human body motion tracking and scanning, in 3D scene analysis of traffic scenes, in industrial applications and many more. The basic concepts rely on mathematical representations of central perspective viewing as they are widely known from photogrammetry or computer vision approaches. The objectives of these methods differ, more or less, from high precision and well-structured measurements in (industrial) photogrammetry to fully-automated non-structured applications in computer vision. Accuracy and precision is a critical issue for the 3D measurement of industrial, engineering or medical objects. As state of the art, photogrammetric multi-view measurements achieve relative precisions in the order of 1:100000 to 1:200000, and relative accuracies with respect to retraceable lengths in the order of 1:50000 to 1:100000 of the largest object diameter. In order to obtain these figures a number of influencing parameters have to be optimized. These are, besides others: physical representation of object surface (targets, texture), illumination and light sources, imaging sensors, cameras and lenses, calibration strategies (camera model), orientation strategies (bundle adjustment), image processing of homologue features (target measurement, stereo and multi-image matching), representation of object or workpiece coordinate systems and object scale. The paper discusses the above mentioned parameters and offers strategies for obtaining highest accuracy in object space. Practical examples of high-quality stereo camera measurements and multi-image applications are used to prove the relevance of high accuracy in different applications, ranging from medical navigation to static and dynamic industrial measurements. In addition, standards for accuracy verifications are presented and demonstrated by practical examples

  20. Combining data fusion with multiresolution analysis for improving the classification accuracy of uterine EMG signals

    NASA Astrophysics Data System (ADS)

    Moslem, Bassam; Diab, Mohamad; Khalil, Mohamad; Marque, Catherine

    2012-12-01

    Multisensor data fusion is a powerful solution for solving difficult pattern recognition problems such as the classification of bioelectrical signals. It is the process of combining information from different sensors to provide a more stable and more robust classification decisions. We combine here data fusion with multiresolution analysis based on the wavelet packet transform (WPT) in order to classify real uterine electromyogram (EMG) signals recorded by 16 electrodes. Herein, the data fusion is done at the decision level by using a weighted majority voting (WMV) rule. On the other hand, the WPT is used to achieve significant enhancement in the classification performance of each channel by improving the discrimination power of the selected feature. We show that the proposed approach tested on our recorded data can improve the recognition accuracy in labor prediction and has a competitive and promising performance.

  1. Attribute-Level and Pattern-Level Classification Consistency and Accuracy Indices for Cognitive Diagnostic Assessment

    ERIC Educational Resources Information Center

    Wang, Wenyi; Song, Lihong; Chen, Ping; Meng, Yaru; Ding, Shuliang

    2015-01-01

    Classification consistency and accuracy are viewed as important indicators for evaluating the reliability and validity of classification results in cognitive diagnostic assessment (CDA). Pattern-level classification consistency and accuracy indices were introduced by Cui, Gierl, and Chang. However, the indices at the attribute level have not yet…

  2. Demographic corrections appear to compromise classification accuracy for severely skewed cognitive tests.

    PubMed

    O'Connell, Megan E; Tuokko, Holly; Kadlec, Helena

    2011-04-01

    Demographic corrections for cognitive tests should improve classification accuracy by reducing age or education biases, but empirical support has been equivocal. Using a simulation procedure, we show that creating moderate or extreme skewness in cognitive tests compromises the classification accuracy of demographic corrections, findings that appear replicated within clinical data for the few neuropsychological test scores with an extreme degree of skew. For most neuropsychological tests, the dementia classification accuracy of raw and demographically corrected scores was equivalent. These findings suggest that the dementia classification accuracy of demographic corrections is robust to slight degrees of skew (i.e., skewness <1.5). PMID:21154077

  3. IMPACTS OF PATCH SIZE AND LANDSCAPE HETEROGENEITY ON THEMATIC IMAGE CLASSIFICATION ACCURACY

    EPA Science Inventory

    Impacts of Patch Size and Landscape Heterogeneity on Thematic Image Classification Accuracy.
    Currently, most thematic accuracy assessments of classified remotely sensed images oily account for errors between the various classes employed, at particular pixels of interest, thu...

  4. Growth in Mathematics Achievement: Analysis with Classification and Regression Trees

    ERIC Educational Resources Information Center

    Ma, Xin

    2005-01-01

    A recently developed statistical technique, often referred to as classification and regression trees (CART), holds great potential for researchers to discover how student-level (and school-level) characteristics interactively affect growth in mathematics achievement. CART is a host of advanced statistical methods that statistically cluster…

  5. Measuring physicians' performance in clinical practice: reliability, classification accuracy, and validity.

    PubMed

    Weifeng Weng; Hess, Brian J; Lynn, Lorna A; Holmboe, Eric S; Lipner, Rebecca S

    2010-09-01

    Much research has been devoted to addressing challenges in achieving reliable assessments of physicians' clinical performance but less work has focused on whether valid and accurate classification decisions are feasible. This study used 957 physicians certified in internal medicine (IM) or a subspecialty, who completed the American Board of Internal Medicine (ABIM) Diabetes Practice Improvement Module (PIM). Ten clinical and two patient-experience measures were aggregated into a composite measure. The composite measure score was highly reliable (r = .91) and classification accuracy was high across the entire score scale (>0.90), which indicated that it is possible to differentiate high-performing and low-performing physicians. Physicians certified in endocrinology and those who scored higher on their IM certification examination had higher composite scores, providing some validity evidence. In summary, it is feasible to create a psychometrically robust composite measure of physicians' clinical performance, specifically for the quality of care they provide to patients with diabetes.

  6. Sampling issues affecting accuracy of likelihood-based classification using genetical data

    USGS Publications Warehouse

    Guinand, B.; Scribner, K.T.; Topchy, A.; Page, K.S.; Punch, W.; Burnham-Curtis, M. K.

    2004-01-01

    We demonstrate the effectiveness of a genetic algorithm for discovering multi-locus combinations that provide accurate individual assignment decisions and estimates of mixture composition based on likelihood classification. Using simulated data representing different levels of inter-population differentiation (Fst ~ 0.01 and 0.10), genetic diversities (four or eight alleles per locus), and population sizes (20, 40, 100 individuals in baseline populations), we show that subsets of loci can be identified that provide comparable levels of accuracy in classification decisions relative to entire multi-locus data sets, where 5, 10, or 20 loci were considered. Microsatellite data sets from hatchery strains of lake trout, Salvelinus namaycush, representing a comparable range of inter-population levels of differentiation in allele frequencies confirmed simulation results. For both simulated and empirical data sets, assignment accuracy was achieved using fewer loci (e.g., three or four loci out of eight for empirical lake trout studies). Simulation results were used to investigate properties of the 'leave-one-out' (L1O) method for estimating assignment error rates. Accuracy of population assignments based on L1O methods should be viewed with caution under certain conditions, particularly when baseline population sample sizes are low (<50).

  7. ASSESSMENT OF LANDSCAPE CHARACTERISTICS ON THEMATIC IMAGE CLASSIFICATION ACCURACY

    EPA Science Inventory

    Landscape characteristics such as small patch size and land cover heterogeneity have been hypothesized to increase the likelihood of misclassifying pixels during thematic image classification. However, there has been a lack of empirical evidence, to support these hypotheses. This...

  8. Improved accuracy of radar WPMM estimated rainfall upon application of objective classification criteria

    NASA Technical Reports Server (NTRS)

    Rosenfeld, Daniel; Amitai, Eyal; Wolff, David B.

    1995-01-01

    Application of the window probability matching method to radar and rain gauge data that have been objectively classified into different rain types resulted in distinctly different Z(sub e)-R relationships for the various classifications. These classification parameters, in addition to the range from the radar, are (a) the horizontal radial reflectivity gradients (dB/km); (b) the cloud depth, as scaled by the effective efficiency; (c) the brightband fraction within the radar field window; and (d) the height of the freezing level. Combining physical parameters to identify the type of precipitation and statistical relations most appropriate to the precipitation types results in considerable improvement of both point and areal rainfall measurements. A limiting factor in the assessment of the improved accuracy is the inherent variance between the true rain intensity at the radar measured volume and the rain intensity at the mouth of the rain guage. Therefore, a very dense rain gauge network is required to validate most of the suggested realized improvement. A rather small sample size is required to achieve a stable Z(sub e)-R relationship (standard deviation of 15% of R for a given Z(sub e)) -- about 200 mm of rainfall accumulated in all guages combined for each classification.

  9. Feature Selection Has a Large Impact on One-Class Classification Accuracy for MicroRNAs in Plants

    PubMed Central

    Yousef, Malik; Saçar Demirci, Müşerref Duygu; Khalifa, Waleed; Allmer, Jens

    2016-01-01

    MicroRNAs (miRNAs) are short RNA sequences involved in posttranscriptional gene regulation. Their experimental analysis is complicated and, therefore, needs to be supplemented with computational miRNA detection. Currently computational miRNA detection is mainly performed using machine learning and in particular two-class classification. For machine learning, the miRNAs need to be parametrized and more than 700 features have been described. Positive training examples for machine learning are readily available, but negative data is hard to come by. Therefore, it seems prerogative to use one-class classification instead of two-class classification. Previously, we were able to almost reach two-class classification accuracy using one-class classifiers. In this work, we employ feature selection procedures in conjunction with one-class classification and show that there is up to 36% difference in accuracy among these feature selection methods. The best feature set allowed the training of a one-class classifier which achieved an average accuracy of ~95.6% thereby outperforming previous two-class-based plant miRNA detection approaches by about 0.5%. We believe that this can be improved upon in the future by rigorous filtering of the positive training examples and by improving current feature clustering algorithms to better target pre-miRNA feature selection. PMID:27190509

  10. Comparison of wheat classification accuracy using different classifiers of the image-100 system

    NASA Technical Reports Server (NTRS)

    Dejesusparada, N. (Principal Investigator); Chen, S. C.; Moreira, M. A.; Delima, A. M.

    1981-01-01

    Classification results using single-cell and multi-cell signature acquisition options, a point-by-point Gaussian maximum-likelihood classifier, and K-means clustering of the Image-100 system are presented. Conclusions reached are that: a better indication of correct classification can be provided by using a test area which contains various cover types of the study area; classification accuracy should be evaluated considering both the percentages of correct classification and error of commission; supervised classification approaches are better than K-means clustering; Gaussian distribution maximum likelihood classifier is better than Single-cell and Multi-cell Signature Acquisition Options of the Image-100 system; and in order to obtain a high classification accuracy in a large and heterogeneous crop area, using Gaussian maximum-likelihood classifier, homogeneous spectral subclasses of the study crop should be created to derive training statistics.

  11. Practical Issues in Estimating Classification Accuracy and Consistency with R Package cacIRT

    ERIC Educational Resources Information Center

    Lathrop, Quinn N.

    2015-01-01

    There are two main lines of research in estimating classification accuracy (CA) and classification consistency (CC) under Item Response Theory (IRT). The R package cacIRT provides computer implementations of both approaches in an accessible and unified framework. Even with available implementations, there remains decisions a researcher faces when…

  12. Does Maximizing Information at the Cut Score Always Maximize Classification Accuracy and Consistency?

    ERIC Educational Resources Information Center

    Wyse, Adam E.; Babcock, Ben

    2016-01-01

    A common suggestion made in the psychometric literature for fixed-length classification tests is that one should design tests so that they have maximum information at the cut score. Designing tests in this way is believed to maximize the classification accuracy and consistency of the assessment. This article uses simulated examples to illustrate…

  13. A coefficient of agreement as a measure of thematic classification accuracy.

    USGS Publications Warehouse

    Rosenfield, G.H.; Fitzpatrick-Lins, K.

    1986-01-01

    The classification error matrix typically contains tabulated results of accuracy evaluation for a thematic classification, such as a land-use and land-cover map. Diagonal elements of the matrix represent counts correct. The usual designation of classification accuracy has been total percent correct. Nondiagonal elements of the matrix have usually been neglected. A coefficient of agreement is determined for the interpreted map as a whole, and individually for each interpreted category. These coefficients utilize all cell values in the matrix.-from Authors

  14. Forest Classification Accuracy as Influenced by Multispectral Scanner Spatial Resolution. [Sam Houston National Forest, Texas

    NASA Technical Reports Server (NTRS)

    Nalepka, R. F. (Principal Investigator); Sadowski, F. E.; Sarno, J. E.

    1976-01-01

    The author has identified the following significant results. A supervised classification within two separate ground areas of the Sam Houston National Forest was carried out for two sq meters spatial resolution MSS data. Data were progressively coarsened to simulate five additional cases of spatial resolution ranging up to 64 sq meters. Similar processing and analysis of all spatial resolutions enabled evaluations of the effect of spatial resolution on classification accuracy for various levels of detail and the effects on area proportion estimation for very general forest features. For very coarse resolutions, a subset of spectral channels which simulated the proposed thematic mapper channels was used to study classification accuracy.

  15. Effect of Pansharpened Image on Some of Pixel Based and Object Based Classification Accuracy

    NASA Astrophysics Data System (ADS)

    Karakus, P.; Karabork, H.

    2016-06-01

    Classification is the most important method to determine type of crop contained in a region for agricultural planning. There are two types of the classification. First is pixel based and the other is object based classification method. While pixel based classification methods are based on the information in each pixel, object based classification method is based on objects or image objects that formed by the combination of information from a set of similar pixels. Multispectral image contains a higher degree of spectral resolution than a panchromatic image. Panchromatic image have a higher spatial resolution than a multispectral image. Pan sharpening is a process of merging high spatial resolution panchromatic and high spectral resolution multispectral imagery to create a single high resolution color image. The aim of the study was to compare the potential classification accuracy provided by pan sharpened image. In this study, SPOT 5 image was used dated April 2013. 5m panchromatic image and 10m multispectral image are pan sharpened. Four different classification methods were investigated: maximum likelihood, decision tree, support vector machine at the pixel level and object based classification methods. SPOT 5 pan sharpened image was used to classification sun flowers and corn in a study site located at Kadirli region on Osmaniye in Turkey. The effects of pan sharpened image on classification results were also examined. Accuracy assessment showed that the object based classification resulted in the better overall accuracy values than the others. The results that indicate that these classification methods can be used for identifying sun flower and corn and estimating crop areas.

  16. The effect of spatial, spectral and radiometric factors on classification accuracy using thematic mapper data

    NASA Technical Reports Server (NTRS)

    Wrigley, R. C.; Acevedo, W.; Alexander, D.; Buis, J.; Card, D.

    1984-01-01

    An experiment of a factorial design was conducted to test the effects on classification accuracy of land cover types due to the improved spatial, spectral and radiometric characteristics of the Thematic Mapper (TM) in comparison to the Multispectral Scanner (MSS). High altitude aircraft scanner data from the Airborne Thematic Mapper instrument was acquired over central California in August, 1983 and used to simulate Thematic Mapper data as well as all combinations of the three characteristics for eight data sets in all. Results for the training sites (field center pixels) showed better classification accuracies for MSS spatial resolution, TM spectral bands and TM radiometry in order of importance.

  17. Assessing the accuracy of Landsat Thematic Mapper classification using double sampling

    USGS Publications Warehouse

    Kalkhan, M.A.; Reich, R.M.; Stohlgren, T.J.

    1998-01-01

    Double sampling was used to provide a cost efficient estimate of the accuracy of a Landsat Thematic Mapper (TM) classification map of a scene located in the Rocky Moutnain National Park, Colorado. In the first phase, 200 sample points were randomly selected to assess the accuracy between Landsat TM data and aerial photography. The overall accuracy and Kappa statistic were 49.5% and 32.5%, respectively. In the second phase, 25 sample points identified in the first phase were selected using stratified random sampling and located in the field. This information was used to correct for misclassification errors associated with the first phase samples. The overall accuracy and Kappa statistic increased to 59.6% and 45.6%, respectively.Double sampling was used to provide a cost efficient estimate of the accuracy of a Landsat Thematic Mapper (TM) classification map of a scene located in the Rocky Mountain National Park, Colorado. In the first phase, 200 sample points were randomly selected to assess the accuracy between Landsat TM data and aerial photography. The overall accuracy and Kappa statistic were 49.5 per cent and 32.5 per cent, respectively. In the second phase, 25 sample points identified in the first phase were selected using stratified random sampling and located in the field. This information was used to correct for misclassification errors associated with the first phase samples. The overall accuracy and Kappa statistic increased to 59.6 per cent and 45.6 per cent, respectively.

  18. Effects of sample survey design on the accuracy of classification tree models in species distribution models

    USGS Publications Warehouse

    Edwards, T.C.; Cutler, D.R.; Zimmermann, N.E.; Geiser, L.; Moisen, G.G.

    2006-01-01

    We evaluated the effects of probabilistic (hereafter DESIGN) and non-probabilistic (PURPOSIVE) sample surveys on resultant classification tree models for predicting the presence of four lichen species in the Pacific Northwest, USA. Models derived from both survey forms were assessed using an independent data set (EVALUATION). Measures of accuracy as gauged by resubstitution rates were similar for each lichen species irrespective of the underlying sample survey form. Cross-validation estimates of prediction accuracies were lower than resubstitution accuracies for all species and both design types, and in all cases were closer to the true prediction accuracies based on the EVALUATION data set. We argue that greater emphasis should be placed on calculating and reporting cross-validation accuracy rates rather than simple resubstitution accuracy rates. Evaluation of the DESIGN and PURPOSIVE tree models on the EVALUATION data set shows significantly lower prediction accuracy for the PURPOSIVE tree models relative to the DESIGN models, indicating that non-probabilistic sample surveys may generate models with limited predictive capability. These differences were consistent across all four lichen species, with 11 of the 12 possible species and sample survey type comparisons having significantly lower accuracy rates. Some differences in accuracy were as large as 50%. The classification tree structures also differed considerably both among and within the modelled species, depending on the sample survey form. Overlap in the predictor variables selected by the DESIGN and PURPOSIVE tree models ranged from only 20% to 38%, indicating the classification trees fit the two evaluated survey forms on different sets of predictor variables. The magnitude of these differences in predictor variables throws doubt on ecological interpretation derived from prediction models based on non-probabilistic sample surveys. ?? 2006 Elsevier B.V. All rights reserved.

  19. Classification Accuracy of Nonword Repetition when Used with Preschool-Age Spanish-Speaking Children

    ERIC Educational Resources Information Center

    Guiberson, Mark; Rodriguez, Barbara L.

    2013-01-01

    Purpose: The purpose of the present study was to (a) describe and compare the nonword repetition (NWR) performance of preschool-age Spanish-speaking children (3- to 5-year-olds) with and without language impairment (LI) across 2 scoring approaches and (b) to contrast the classification accuracy of a Spanish NWR task when item-level and percentage…

  20. Examining the Classification Accuracy of a Vocabulary Screening Measure with Preschool Children

    ERIC Educational Resources Information Center

    Marcotte, Amanda M.; Clemens, Nathan H.; Parker, Christopher; Whitcomb, Sara A.

    2016-01-01

    This study investigated the classification accuracy of the "Dynamic Indicators of Vocabulary Skills" (DIVS) as a preschool vocabulary screening measure. With a sample of 240 preschoolers, fall and winter DIVS scores were used to predict year-end vocabulary risk using the 25th percentile on the "Peabody Picture Vocabulary Test--Third…

  1. Classification Accuracy of Brief Parent Report Measures of Language Development in Spanish-Speaking Toddlers

    ERIC Educational Resources Information Center

    Guiberson, Mark; Rodriguez, Barbara L.; Dale, Philip S.

    2011-01-01

    Purpose: The purpose of the current study was to examine the concurrent validity and classification accuracy of 3 parent report measures of language development in Spanish-speaking toddlers. Method: Forty-five Spanish-speaking parents and their 2-year-old children participated. Twenty-three children had expressive language delays (ELDs) as…

  2. Two Approaches to Estimation of Classification Accuracy Rate under Item Response Theory

    ERIC Educational Resources Information Center

    Lathrop, Quinn N.; Cheng, Ying

    2013-01-01

    Within the framework of item response theory (IRT), there are two recent lines of work on the estimation of classification accuracy (CA) rate. One approach estimates CA when decisions are made based on total sum scores, the other based on latent trait estimates. The former is referred to as the Lee approach, and the latter, the Rudner approach,…

  3. Assessing the Accuracy and Consistency of Language Proficiency Classification under Competing Measurement Models

    ERIC Educational Resources Information Center

    Zhang, Bo

    2010-01-01

    This article investigates how measurement models and statistical procedures can be applied to estimate the accuracy of proficiency classification in language testing. The paper starts with a concise introduction of four measurement models: the classical test theory (CTT) model, the dichotomous item response theory (IRT) model, the testlet response…

  4. Predictive Utility and Classification Accuracy of Oral Reading Fluency and the Measures of Academic Progress for the Wisconsin Knowledge and Concepts Exam

    ERIC Educational Resources Information Center

    Ball, Carrie R.; O'Connor, Edward

    2016-01-01

    This study examined the predictive validity and classification accuracy of two commonly used universal screening measures relative to a statewide achievement test. Results indicated that second-grade performance on oral reading fluency and the Measures of Academic Progress (MAP), together with special education status, explained 68% of the…

  5. Effects of autocorrelation upon LANDSAT classification accuracy. [Richmond, Virginia and Denver, Colorado

    NASA Technical Reports Server (NTRS)

    Craig, R. G. (Principal Investigator)

    1983-01-01

    Richmond, Virginia and Denver, Colorado were study sites in an effort to determine the effect of autocorrelation on the accuracy of a parallelopiped classifier of LANDSAT digital data. The autocorrelation was assumed to decay to insignificant levels when sampled at distances of at least ten pixels. Spectral themes developed using blocks of adjacent pixels, and using groups of pixels spaced at least 10 pixels apart were used. Effects of geometric distortions were minimized by using only pixels from the interiors of land cover sections. Accuracy was evaluated for three classes; agriculture, residential and "all other"; both type 1 and type 2 errors were evaluated by means of overall classification accuracy. All classes give comparable results. Accuracy is approximately the same in both techniques; however, the variance in accuracy is significantly higher using the themes developed from autocorrelated data. The vectors of mean spectral response were nearly identical regardless of sampling method used. The estimated variances were much larger when using autocorrelated pixels.

  6. Study on Increasing the Accuracy of Classification Based on Ant Colony algorithm

    NASA Astrophysics Data System (ADS)

    Yu, M.; Chen, D.-W.; Dai, C.-Y.; Li, Z.-L.

    2013-05-01

    The application for GIS advances the ability of data analysis on remote sensing image. The classification and distill of remote sensing image is the primary information source for GIS in LUCC application. How to increase the accuracy of classification is an important content of remote sensing research. Adding features and researching new classification methods are the ways to improve accuracy of classification. Ant colony algorithm based on mode framework defined, agents of the algorithms in nature-inspired computation field can show a kind of uniform intelligent computation mode. It is applied in remote sensing image classification is a new method of preliminary swarm intelligence. Studying the applicability of ant colony algorithm based on more features and exploring the advantages and performance of ant colony algorithm are provided with very important significance. The study takes the outskirts of Fuzhou with complicated land use in Fujian Province as study area. The multi-source database which contains the integration of spectral information (TM1-5, TM7, NDVI, NDBI) and topography characters (DEM, Slope, Aspect) and textural information (Mean, Variance, Homogeneity, Contrast, Dissimilarity, Entropy, Second Moment, Correlation) were built. Classification rules based different characters are discovered from the samples through ant colony algorithm and the classification test is performed based on these rules. At the same time, we compare with traditional maximum likelihood method, C4.5 algorithm and rough sets classifications for checking over the accuracies. The study showed that the accuracy of classification based on the ant colony algorithm is higher than other methods. In addition, the land use and cover changes in Fuzhou for the near term is studied and display the figures by using remote sensing technology based on ant colony algorithm. In addition, the land use and cover changes in Fuzhou for the near term is studied and display the figures by using

  7. Additional studies of forest classification accuracy as influenced by multispectral scanner spatial resolution

    NASA Technical Reports Server (NTRS)

    Sadowski, F. E.; Sarno, J. E.

    1976-01-01

    First, an analysis of forest feature signatures was used to help explain the large variation in classification accuracy that can occur among individual forest features for any one case of spatial resolution and the inconsistent changes in classification accuracy that were demonstrated among features as spatial resolution was degraded. Second, the classification rejection threshold was varied in an effort to reduce the large proportion of unclassified resolution elements that previously appeared in the processing of coarse resolution data when a constant rejection threshold was used for all cases of spatial resolution. For the signature analysis, two-channel ellipse plots showing the feature signature distributions for several cases of spatial resolution indicated that the capability of signatures to correctly identify their respective features is dependent on the amount of statistical overlap among signatures. Reductions in signature variance that occur in data of degraded spatial resolution may not necessarily decrease the amount of statistical overlap among signatures having large variance and small mean separations. Features classified by such signatures may thus continue to have similar amounts of misclassified elements in coarser resolution data, and thus, not necessarily improve in classification accuracy.

  8. Assessment of Classification Accuracies of SENTINEL-2 and LANDSAT-8 Data for Land Cover / Use Mapping

    NASA Astrophysics Data System (ADS)

    Hale Topaloğlu, Raziye; Sertel, Elif; Musaoğlu, Nebiye

    2016-06-01

    This study aims to compare classification accuracies of land cover/use maps created from Sentinel-2 and Landsat-8 data. Istanbul metropolitan city of Turkey, with a population of around 14 million, having different landscape characteristics was selected as study area. Water, forest, agricultural areas, grasslands, transport network, urban, airport- industrial units and barren land- mine land cover/use classes adapted from CORINE nomenclature were used as main land cover/use classes to identify. To fulfil the aims of this research, recently acquired dated 08/02/2016 Sentinel-2 and dated 22/02/2016 Landsat-8 images of Istanbul were obtained and image pre-processing steps like atmospheric and geometric correction were employed. Both Sentinel-2 and Landsat-8 images were resampled to 30m pixel size after geometric correction and similar spectral bands for both satellites were selected to create a similar base for these multi-sensor data. Maximum Likelihood (MLC) and Support Vector Machine (SVM) supervised classification methods were applied to both data sets to accurately identify eight different land cover/ use classes. Error matrix was created using same reference points for Sentinel-2 and Landsat-8 classifications. After the classification accuracy, results were compared to find out the best approach to create current land cover/use map of the region. The results of MLC and SVM classification methods were compared for both images.

  9. Agricultural case studies of classification accuracy, spectral resolution, and model over-fitting.

    PubMed

    Nansen, Christian; Geremias, Leandro Delalibera; Xue, Yingen; Huang, Fangneng; Parra, Jose Roberto

    2013-11-01

    This paper describes the relationship between spectral resolution and classification accuracy in analyses of hyperspectral imaging data acquired from crop leaves. The main scope is to discuss and reduce the risk of model over-fitting. Over-fitting of a classification model occurs when too many and/or irrelevant model terms are included (i.e., a large number of spectral bands), and it may lead to low robustness/repeatability when the classification model is applied to independent validation data. We outline a simple way to quantify the level of model over-fitting by comparing the observed classification accuracies with those obtained from explanatory random data. Hyperspectral imaging data were acquired from two crop-insect pest systems: (1) potato psyllid (Bactericera cockerelli) infestations of individual bell pepper plants (Capsicum annuum) with the acquisition of hyperspectral imaging data under controlled-light conditions (data set 1), and (2) sugarcane borer (Diatraea saccharalis) infestations of individual maize plants (Zea mays) with the acquisition of hyperspectral imaging data from the same plants under two markedly different image-acquisition conditions (data sets 2a and b). For each data set, reflectance data were analyzed based on seven spectral resolutions by dividing 160 spectral bands from 405 to 907 nm into 4, 16, 32, 40, 53, 80, or 160 bands. In the two data sets, similar classification results were obtained with spectral resolutions ranging from 3.1 to 12.6 nm. Thus, the size of the initial input data could be reduced fourfold with only a negligible loss of classification accuracy. In the analysis of data set 1, several validation approaches all demonstrated consistently that insect-induced stress could be accurately detected and that therefore there was little indication of model over-fitting. In the analyses of data set 2, inconsistent validation results were obtained and the observed classification accuracy (81.06%) was only a few percentage

  10. Investigation of the trade-off between time window length, classifier update rate and classification accuracy for restorative brain-computer interfaces.

    PubMed

    Darvishi, Sam; Ridding, Michael C; Abbott, Derek; Baumert, Mathias

    2013-01-01

    Recently, the application of restorative brain-computer interfaces (BCIs) has received significant interest in many BCI labs. However, there are a number of challenges, that need to be tackled to achieve efficient performance of such systems. For instance, any restorative BCI needs an optimum trade-off between time window length, classification accuracy and classifier update rate. In this study, we have investigated possible solutions to these problems by using a dataset provided by the University of Graz, Austria. We have used a continuous wavelet transform and the Student t-test for feature extraction and a support vector machine (SVM) for classification. We find that improved results, for restorative BCIs for rehabilitation, may be achieved by using a 750 milliseconds time window with an average classification accuracy of 67% that updates every 32 milliseconds.

  11. Improved reticle requalification accuracy and efficiency via simulation-powered automated defect classification

    NASA Astrophysics Data System (ADS)

    Paracha, Shazad; Eynon, Benjamin; Noyes, Ben F.; Nhiev, Anthony; Vacca, Anthony; Fiekowsky, Peter; Fiekowsky, Dan; Ham, Young Mog; Uzzel, Doug; Green, Michael; MacDonald, Susan; Morgan, John

    2014-04-01

    Advanced IC fabs must inspect critical reticles on a frequent basis to ensure high wafer yields. These necessary requalification inspections have traditionally carried high risk and expense. Manually reviewing sometimes hundreds of potentially yield-limiting detections is a very high-risk activity due to the likelihood of human error; the worst of which is the accidental passing of a real, yield-limiting defect. Painfully high cost is incurred as a result, but high cost is also realized on a daily basis while reticles are being manually classified on inspection tools since these tools often remain in a non-productive state during classification. An automatic defect analysis system (ADAS) has been implemented at a 20nm node wafer fab to automate reticle defect classification by simulating each defect's printability under the intended illumination conditions. In this paper, we have studied and present results showing the positive impact that an automated reticle defect classification system has on the reticle requalification process; specifically to defect classification speed and accuracy. To verify accuracy, detected defects of interest were analyzed with lithographic simulation software and compared to the results of both AIMS™ optical simulation and to actual wafer prints.

  12. Classification of 1H MR spectra of human brain neoplasms: the influence of preprocessing and computerized consensus diagnosis on classification accuracy.

    PubMed

    Somorjai, R L; Dolenko, B; Nikulin, A K; Pizzi, N; Scarth, G; Zhilkin, P; Halliday, W; Fewer, D; Hill, N; Ross, I; West, M; Smith, I C; Donnelly, S M; Kuesel, A C; Brière, K M

    1996-01-01

    We study how classification accuracy can be improved when both different data preprocessing methods and computerized consensus diagnosis (CCD) are applied to 1H magnetic resonance (MR) spectra of astrocytomas, meningiomas, and epileptic brain tissue. The MR spectra (360 MHz, 37 degrees C) of tissue specimens (biopsies) from subjects with meningiomas (95; 26 cases), astrocytomas (74; 26 cases), and epilepsy (37; 8 cases) were preprocessed by several methods. Each data set was partitioned into training and validation sets. Robust classification was carried out via linear discriminant analysis (LDA), artificial neural nets (NN), and CCD, and the results were compared with histopathological diagnosis of the MR specimens. Normalization of the relevant spectral regions affects classification accuracy significantly. The spectra-based average three-class classification accuracies of LDA and NN increased from 81.7% (unnormalized data sets) to 89.9% (normalized). CCD increased the classification accuracy of the normalized sets to an average of 91.8%. CCD invariably decreases the fraction of unclassifiable spectra. The same trends prevail, with improved results, for case-based classification. Preprocessing the 1H MR spectra is essential for accurate and reliable classification of astrocytomas, meningiomas, and nontumorous epileptic brain tissue. CCD improves classification accuracy, with an attendant decrease in the fraction of unclassifiable spectra or cases.

  13. A retrospective study to validate an intraoperative robotic classification system for assessing the accuracy of kirschner wire (K-wire) placements with postoperative computed tomography classification system for assessing the accuracy of pedicle screw placements

    PubMed Central

    Tsai, Tai-Hsin; Wu, Dong-Syuan; Su, Yu-Feng; Wu, Chieh-Hsin; Lin, Chih-Lung

    2016-01-01

    Abstract This purpose of this retrospective study is validation of an intraoperative robotic grading classification system for assessing the accuracy of Kirschner-wire (K-wire) placements with the postoperative computed tomography (CT)-base classification system for assessing the accuracy of pedicle screw placements. We conducted a retrospective review of prospectively collected data from 35 consecutive patients who underwent 176 robotic assisted pedicle screws instrumentation at Kaohsiung Medical University Hospital from September 2014 to November 2015. During the operation, we used a robotic grading classification system for verifying the intraoperative accuracy of K-wire placements. Three months after surgery, we used the common CT-base classification system to assess the postoperative accuracy of pedicle screw placements. The distributions of accuracy between the intraoperative robot-assisted and various postoperative CT-based classification systems were compared using kappa statistics of agreement. The intraoperative accuracies of K-wire placements before and after repositioning were classified as excellent (131/176, 74.4% and 133/176, 75.6%, respectively), satisfactory (36/176, 20.5% and 41/176, 23.3%, respectively), and malpositioned (9/176, 5.1% and 2/176, 1.1%, respectively) In postoperative CT-base classification systems were evaluated. No screw placements were evaluated as unacceptable under any of these systems. Kappa statistics revealed no significant differences between the proposed system and the aforementioned classification systems (P <0.001). Our results revealed no significant differences between the intraoperative robotic grading system and various postoperative CT-based grading systems. The robotic grading classification system is a feasible method for evaluating the accuracy of K-wire placements. Using the intraoperative robot grading system to classify the accuracy of K-wire placements enables predicting the postoperative accuracy of

  14. A retrospective study to validate an intraoperative robotic classification system for assessing the accuracy of kirschner wire (K-wire) placements with postoperative computed tomography classification system for assessing the accuracy of pedicle screw placements.

    PubMed

    Tsai, Tai-Hsin; Wu, Dong-Syuan; Su, Yu-Feng; Wu, Chieh-Hsin; Lin, Chih-Lung

    2016-09-01

    This purpose of this retrospective study is validation of an intraoperative robotic grading classification system for assessing the accuracy of Kirschner-wire (K-wire) placements with the postoperative computed tomography (CT)-base classification system for assessing the accuracy of pedicle screw placements.We conducted a retrospective review of prospectively collected data from 35 consecutive patients who underwent 176 robotic assisted pedicle screws instrumentation at Kaohsiung Medical University Hospital from September 2014 to November 2015. During the operation, we used a robotic grading classification system for verifying the intraoperative accuracy of K-wire placements. Three months after surgery, we used the common CT-base classification system to assess the postoperative accuracy of pedicle screw placements. The distributions of accuracy between the intraoperative robot-assisted and various postoperative CT-based classification systems were compared using kappa statistics of agreement.The intraoperative accuracies of K-wire placements before and after repositioning were classified as excellent (131/176, 74.4% and 133/176, 75.6%, respectively), satisfactory (36/176, 20.5% and 41/176, 23.3%, respectively), and malpositioned (9/176, 5.1% and 2/176, 1.1%, respectively)In postoperative CT-base classification systems were evaluated. No screw placements were evaluated as unacceptable under any of these systems. Kappa statistics revealed no significant differences between the proposed system and the aforementioned classification systems (P <0.001).Our results revealed no significant differences between the intraoperative robotic grading system and various postoperative CT-based grading systems. The robotic grading classification system is a feasible method for evaluating the accuracy of K-wire placements. Using the intraoperative robot grading system to classify the accuracy of K-wire placements enables predicting the postoperative accuracy of pedicle screw

  15. Accuracy of supervised classification of cropland in sub-Saharan Africa

    NASA Astrophysics Data System (ADS)

    Lewis-Gonzales, S. L.; Nagle, N. N.; Grace, K.

    2013-12-01

    Identifying cropland is key to assessing food security hazards. Methods of identifying cropland usually fuse together readily available, but unclassified imagery, with labeled ground truth data from small training areas. We conduct a supervised classification of Landsat TM surface reflectance products for sub-Saharan Mali. Training data are provided by visual interpretation of high resolution (sub-meter) imagery. Due to the expense of acquiring and manually classifiying such data, it is infeasible to acquire many training samples. Thus, a crucial concern is determining the classification accuracy in regions where training data are sparse. We characterize this classification accuracy across the study region, as a function of geographic distance from training samples and as a function of landcover fragmentation. In regards to research involving food security and environmental deterioration crises, this method not only increases the overall scale at which a study can cover but also allows these assessments to take place in hostile and/or dangerous areas where food security and environmental crises are common.

  16. Hyperspectral image preprocessing with bilateral filter for improving the classification accuracy of support vector machines

    NASA Astrophysics Data System (ADS)

    Sahadevan, Anand S.; Routray, Aurobinda; Das, Bhabani S.; Ahmad, Saquib

    2016-04-01

    Bilateral filter (BF) theory is applied to integrate spatial contextual information into the spectral domain for improving the accuracy of the support vector machine (SVM) classifier. The proposed classification framework is a two-stage process. First, an edge-preserved smoothing is carried out on a hyperspectral image (HSI). Then, the SVM multiclass classifier is applied on the smoothed HSI. One of the advantages of the BF-based implementation is that it considers the spatial as well as spectral closeness for smoothing the HSI. Therefore, the proposed method provides better smoothing in the homogeneous region and preserves the image details, which in turn improves the separability between the classes. The performance of the proposed method is tested using benchmark HSIs obtained from the airborne-visible-infrared-imaging-spectrometer (AVIRIS) and the reflective-optics-system-imaging-spectrometer (ROSIS) sensors. Experimental results demonstrate the effectiveness of the edge-preserved filtering in the classification of the HSI. Average accuracies (with 10% training samples) of the proposed classification framework are 99.04%, 98.11%, and 96.42% for AVIRIS-Salinas, ROSIS-Pavia University, and AVIRIS-Indian Pines images, respectively. Since the proposed method follows a combination of BF and the SVM formulations, it will be quite simple and practical to implement in real applications.

  17. The Accuracy of Body Mass Index and Gallagher’s Classification in Detecting Obesity among Iranians

    PubMed Central

    Jahanlou, Alireza Shahab; Kouzekanani, Kamiar

    2016-01-01

    Background: The study was conducted to examine the comparability of the BMI and Gallagher’s classification in diagnosing obesity based on the cutoff points of the gold standards and to estimate suitable cutoff points for detecting obesity among Iranians. Methods: The cross-sectional study was comparative in nature. The sample consisted of 20,163 adults. The bioelectrical impedance analysis (BIA) was used to measure the variables of interest. Sensitivity, specificity, positive predictive power (PPV), and negative predictive power (NPV) were used to evaluate the comparability of the two classification methods in detecting obesity. Results: The BMI wrongly classified 29% of the obese persons as overweight. In both classifications, as age increased, the accuracy of detecting obesity decreased. The Gallagher’s classification is better than MBI in detecting obesity in men with the exception of those older than 59 years. In females, the BMI was better in determining sensitivity. In both classifications, either female or male, an increase in age was associated with a decrease in sensitivity and NPV with the exception of the BMI for the 18 year olds. Gallagher can correctly classify males and females who are less than 40 and 19 years old, respectively. Conclusion: Gallagher’s classification is recommended for non-obese in both sexes and in obese males younger than 40 years old. The BMI is recommended for obese females. The suitable cutoff points for the BMI to detect obesity are 27.70 kg/m2 for females and males, 27.70 kg/m2 for females, and 27.30 kg/m2 for males. PMID:27365550

  18. Classification Accuracy of Mixed Format Tests: A Bi-Factor Item Response Theory Approach

    PubMed Central

    Wang, Wei; Drasgow, Fritz; Liu, Liwen

    2016-01-01

    Mixed format tests (e.g., a test consisting of multiple-choice [MC] items and constructed response [CR] items) have become increasingly popular. However, the latent structure of item pools consisting of the two formats is still equivocal. Moreover, the implications of this latent structure are unclear: For example, do constructed response items tap reasoning skills that cannot be assessed with multiple choice items? This study explored the dimensionality of mixed format tests by applying bi-factor models to 10 tests of various subjects from the College Board's Advanced Placement (AP) Program and compared the accuracy of scores based on the bi-factor analysis with scores derived from a unidimensional analysis. More importantly, this study focused on a practical and important question—classification accuracy of the overall grade on a mixed format test. Our findings revealed that the degree of multidimensionality resulting from the mixed item format varied from subject to subject, depending on the disattenuated correlation between scores from MC and CR subtests. Moreover, remarkably small decrements in classification accuracy were found for the unidimensional analysis when the disattenuated correlations exceeded 0.90. PMID:26973568

  19. Effects of temporal variability in ground data collection on classification accuracy

    USGS Publications Warehouse

    Hoch, G.A.; Cully, J.F.

    1999-01-01

    This research tested whether the timing of ground data collection can significantly impact the accuracy of land cover classification. Ft. Riley Military Reservation, Kansas, USA was used to test this hypothesis. The U.S. Army's Land Condition Trend Analysis (LCTA) data annually collected at military bases was used to ground truth disturbance patterns. Ground data collected over an entire growing season and data collected one year after the imagery had a kappa statistic of 0.33. When using ground data from only within two weeks of image acquisition the kappa statistic improved to 0.55. Potential sources of this discrepancy are identified. These data demonstrate that there can be significant amounts of land cover change within a narrow time window on military reservations. To accurately conduct land cover classification at military reservations, ground data need to be collected in as narrow a window of time as possible and be closely synchronized with the date of the satellite imagery.

  20. Natural language processing with dynamic classification improves P300 speller accuracy and bit rate

    NASA Astrophysics Data System (ADS)

    Speier, William; Arnold, Corey; Lu, Jessica; Taira, Ricky K.; Pouratian, Nader

    2012-02-01

    The P300 speller is an example of a brain-computer interface that can restore functionality to victims of neuromuscular disorders. Although the most common application of this system has been communicating language, the properties and constraints of the linguistic domain have not to date been exploited when decoding brain signals that pertain to language. We hypothesized that combining the standard stepwise linear discriminant analysis with a Naive Bayes classifier and a trigram language model would increase the speed and accuracy of typing with the P300 speller. With integration of natural language processing, we observed significant improvements in accuracy and 40-60% increases in bit rate for all six subjects in a pilot study. This study suggests that integrating information about the linguistic domain can significantly improve signal classification.

  1. Accuracy assessment, using stratified plurality sampling, of portions of a LANDSAT classification of the Arctic National Wildlife Refuge Coastal Plain

    NASA Technical Reports Server (NTRS)

    Card, Don H.; Strong, Laurence L.

    1989-01-01

    An application of a classification accuracy assessment procedure is described for a vegetation and land cover map prepared by digital image processing of LANDSAT multispectral scanner data. A statistical sampling procedure called Stratified Plurality Sampling was used to assess the accuracy of portions of a map of the Arctic National Wildlife Refuge coastal plain. Results are tabulated as percent correct classification overall as well as per category with associated confidence intervals. Although values of percent correct were disappointingly low for most categories, the study was useful in highlighting sources of classification error and demonstrating shortcomings of the plurality sampling method.

  2. High-Reproducibility and High-Accuracy Method for Automated Topic Classification

    NASA Astrophysics Data System (ADS)

    Lancichinetti, Andrea; Sirer, M. Irmak; Wang, Jane X.; Acuna, Daniel; Körding, Konrad; Amaral, Luís A. Nunes

    2015-01-01

    Much of human knowledge sits in large databases of unstructured text. Leveraging this knowledge requires algorithms that extract and record metadata on unstructured text documents. Assigning topics to documents will enable intelligent searching, statistical characterization, and meaningful classification. Latent Dirichlet allocation (LDA) is the state of the art in topic modeling. Here, we perform a systematic theoretical and numerical analysis that demonstrates that current optimization techniques for LDA often yield results that are not accurate in inferring the most suitable model parameters. Adapting approaches from community detection in networks, we propose a new algorithm that displays high reproducibility and high accuracy and also has high computational efficiency. We apply it to a large set of documents in the English Wikipedia and reveal its hierarchical structure.

  3. Detecting malingered pain-related disability: classification accuracy of the Portland Digit Recognition Test.

    PubMed

    Greve, Kevin W; Bianchini, Kevin J; Etherton, Joseph L; Ord, Jonathan S; Curtis, Kelly L

    2009-07-01

    This study used criterion groups validation to determine the classification accuracy of the Portland Digit Recognition Test (PDRT) at a range of cutting scores in chronic pain patients undergoing psychological evaluation (n = 318), college student simulators (n = 29), and patients with brain damage (n = 120). PDRT scores decreased and failure rates increased as a function of greater independent evidence of intentional underperformance. There were no differences between patients classified as malingering and college student simulators. The PDRT detected from 33% to nearly 60% of malingering chronic pain patients, depending on the cutoff used. False positive error rates ranged from 3% to 6%. Scores higher than the original cutoffs may be interpreted as indicating negative response bias in patients with pain, increasing the usefulness and facilitating the clinical application of the PDRT in the detection of malingering in pain.

  4. Speed and accuracy of facial expression classification in avoidant personality disorder: a preliminary study.

    PubMed

    Rosenthal, M Zachary; Kim, Kwanguk; Herr, Nathaniel R; Smoski, Moria J; Cheavens, Jennifer S; Lynch, Thomas R; Kosson, David S

    2011-10-01

    The aim of this preliminary study was to examine whether individuals with avoidant personality disorder (APD) could be characterized by deficits in the classification of dynamically presented facial emotional expressions. Using a community sample of adults with APD (n = 17) and non-APD controls (n = 16), speed and accuracy of facial emotional expression recognition was investigated in a task that morphs facial expressions from neutral to prototypical expressions (Multi-Morph Facial Affect Recognition Task; Blair, Colledge, Murray, & Mitchell, 2001). Results indicated that individuals with APD were significantly more likely than controls to make errors when classifying fully expressed fear. However, no differences were found between groups in the speed to correctly classify facial emotional expressions. The findings are some of the first to investigate facial emotional processing in a sample of individuals with APD and point to an underlying deficit in processing social cues that may be involved in the maintenance of APD. PMID:22448805

  5. An improved multivariate analytical method to assess the accuracy of acoustic sediment classification maps.

    NASA Astrophysics Data System (ADS)

    Biondo, M.; Bartholomä, A.

    2014-12-01

    High resolution hydro acoustic methods have been successfully employed for the detailed classification of sedimentary habitats. The fine-scale mapping of very heterogeneous, patchy sedimentary facies, and the compound effect of multiple non-linear physical processes on the acoustic signal, cause the classification of backscatter images to be subject to a great level of uncertainty. Standard procedures for assessing the accuracy of acoustic classification maps are not yet established. This study applies different statistical techniques to automated classified acoustic images with the aim of i) quantifying the ability of backscatter to resolve grain size distributions ii) understanding complex patterns influenced by factors other than grain size variations iii) designing innovative repeatable statistical procedures to spatially assess classification uncertainties. A high-frequency (450 kHz) sidescan sonar survey, carried out in the year 2012 in the shallow upper-mesotidal inlet the Jade Bay (German North Sea), allowed to map 100 km2 of surficial sediment with a resolution and coverage never acquired before in the area. The backscatter mosaic was ground-truthed using a large dataset of sediment grab sample information (2009-2011). Multivariate procedures were employed for modelling the relationship between acoustic descriptors and granulometric variables in order to evaluate the correctness of acoustic classes allocation and sediment group separation. Complex patterns in the acoustic signal appeared to be controlled by the combined effect of surface roughness, sorting and mean grain size variations. The area is dominated by silt and fine sand in very mixed compositions; in this fine grained matrix, percentages of gravel resulted to be the prevailing factor affecting backscatter variability. In the absence of coarse material, sorting mostly affected the ability to detect gradual but significant changes in seabed types. Misclassification due to temporal discrepancies

  6. A Response to an Article Published in "Educational Research"'s Special Issue on Assessment (June 2009). What Can Be Inferred about Classification Accuracy from Classification Consistency?

    ERIC Educational Resources Information Center

    Bramley, Tom

    2010-01-01

    Background: A recent article published in "Educational Research" on the reliability of results in National Curriculum testing in England (Newton, "The reliability of results from national curriculum testing in England," "Educational Research" 51, no. 2: 181-212, 2009) suggested that: (1) classification accuracy can be calculated from…

  7. Reliability, Validity, and Classification Accuracy of the DSM-5 Diagnostic Criteria for Gambling Disorder and Comparison to DSM-IV.

    PubMed

    Stinchfield, Randy; McCready, John; Turner, Nigel E; Jimenez-Murcia, Susana; Petry, Nancy M; Grant, Jon; Welte, John; Chapman, Heather; Winters, Ken C

    2016-09-01

    The DSM-5 was published in 2013 and it included two substantive revisions for gambling disorder (GD). These changes are the reduction in the threshold from five to four criteria and elimination of the illegal activities criterion. The purpose of this study was to twofold. First, to assess the reliability, validity and classification accuracy of the DSM-5 diagnostic criteria for GD. Second, to compare the DSM-5-DSM-IV on reliability, validity, and classification accuracy, including an examination of the effect of the elimination of the illegal acts criterion on diagnostic accuracy. To compare DSM-5 and DSM-IV, eight datasets from three different countries (Canada, USA, and Spain; total N = 3247) were used. All datasets were based on similar research methods. Participants were recruited from outpatient gambling treatment services to represent the group with a GD and from the community to represent the group without a GD. All participants were administered a standardized measure of diagnostic criteria. The DSM-5 yielded satisfactory reliability, validity and classification accuracy. In comparing the DSM-5 to the DSM-IV, most comparisons of reliability, validity and classification accuracy showed more similarities than differences. There was evidence of modest improvements in classification accuracy for DSM-5 over DSM-IV, particularly in reduction of false negative errors. This reduction in false negative errors was largely a function of lowering the cut score from five to four and this revision is an improvement over DSM-IV. From a statistical standpoint, eliminating the illegal acts criterion did not make a significant impact on diagnostic accuracy. From a clinical standpoint, illegal acts can still be addressed in the context of the DSM-5 criterion of lying to others. PMID:26408026

  8. Reliability, Validity, and Classification Accuracy of the DSM-5 Diagnostic Criteria for Gambling Disorder and Comparison to DSM-IV.

    PubMed

    Stinchfield, Randy; McCready, John; Turner, Nigel E; Jimenez-Murcia, Susana; Petry, Nancy M; Grant, Jon; Welte, John; Chapman, Heather; Winters, Ken C

    2016-09-01

    The DSM-5 was published in 2013 and it included two substantive revisions for gambling disorder (GD). These changes are the reduction in the threshold from five to four criteria and elimination of the illegal activities criterion. The purpose of this study was to twofold. First, to assess the reliability, validity and classification accuracy of the DSM-5 diagnostic criteria for GD. Second, to compare the DSM-5-DSM-IV on reliability, validity, and classification accuracy, including an examination of the effect of the elimination of the illegal acts criterion on diagnostic accuracy. To compare DSM-5 and DSM-IV, eight datasets from three different countries (Canada, USA, and Spain; total N = 3247) were used. All datasets were based on similar research methods. Participants were recruited from outpatient gambling treatment services to represent the group with a GD and from the community to represent the group without a GD. All participants were administered a standardized measure of diagnostic criteria. The DSM-5 yielded satisfactory reliability, validity and classification accuracy. In comparing the DSM-5 to the DSM-IV, most comparisons of reliability, validity and classification accuracy showed more similarities than differences. There was evidence of modest improvements in classification accuracy for DSM-5 over DSM-IV, particularly in reduction of false negative errors. This reduction in false negative errors was largely a function of lowering the cut score from five to four and this revision is an improvement over DSM-IV. From a statistical standpoint, eliminating the illegal acts criterion did not make a significant impact on diagnostic accuracy. From a clinical standpoint, illegal acts can still be addressed in the context of the DSM-5 criterion of lying to others.

  9. Accuracy and cut-off point selection in three-class classification problems using a generalization of the Youden index.

    PubMed

    Nakas, Christos T; Alonzo, Todd A; Yiannoutsos, Constantin T

    2010-12-10

    We study properties of the index J(3), defined as the accuracy, or the maximum correct classification, for a given three-class classification problem. Specifically, using J(3) one can assess the discrimination between the three distributions and obtain an optimal pair of cut-off points c(1)classification proportions will be maximized. It also serves as the generalization of the Youden index in three-class problems. Parametric and non-parametric approaches for estimation and testing are considered and methods are applied to data from an MRS study on human immunodeficiency virus (HIV) patients.

  10. The Wechsler Adult Intelligence Scale-III and Malingering in Traumatic Brain Injury: Classification Accuracy in Known Groups

    ERIC Educational Resources Information Center

    Curtis, Kelly L.; Greve, Kevin W.; Bianchini, Kevin J.

    2009-01-01

    A known-groups design was used to determine the classification accuracy of Wechsler Adult Intelligence Scale-III (WAIS-III) variables in detecting malingered neurocognitive dysfunction (MND) in traumatic brain injury (TBI). TBI patients were classified into the following groups: (a) mild TBI not-MND (n = 26), (b) mild TBI MND (n = 31), and (c)…

  11. Measurement Properties and Classification Accuracy of Two Spanish Parent Surveys of Language Development for Preschool-Age Children

    ERIC Educational Resources Information Center

    Guiberson, Mark; Rodriguez, Barbara L.

    2010-01-01

    Purpose: To describe the concurrent validity and classification accuracy of 2 Spanish parent surveys of language development, the Spanish Ages and Stages Questionnaire (ASQ; Squires, Potter, & Bricker, 1999) and the Pilot Inventario-III (Pilot INV-III; Guiberson, 2008a). Method: Forty-eight Spanish-speaking parents of preschool-age children…

  12. Classification Accuracy of Oral Reading Fluency and Maze in Predicting Performance on Large-Scale Reading Assessments

    ERIC Educational Resources Information Center

    Decker, Dawn M.; Hixson, Michael D.; Shaw, Amber; Johnson, Gloria

    2014-01-01

    The purpose of this study was to examine whether using a multiple-measure framework yielded better classification accuracy than oral reading fluency (ORF) or maze alone in predicting pass/fail rates for middle-school students on a large-scale reading assessment. Participants were 178 students in Grades 7 and 8 from a Midwestern school district.…

  13. Accuracy of Self-Reported College GPA: Gender-Moderated Differences by Achievement Level and Academic Self-Efficacy

    ERIC Educational Resources Information Center

    Caskie, Grace I. L.; Sutton, MaryAnn C.; Eckhardt, Amanda G.

    2014-01-01

    Assessments of college academic achievement tend to rely on self-reported GPA values, yet evidence is limited regarding the accuracy of those values. With a sample of 194 undergraduate college students, the present study examined whether accuracy of self-reported GPA differed based on level of academic performance or level of academic…

  14. Pseudo-inverse linear discriminants for the improvement of overall classification accuracies.

    PubMed

    Daqi, Gao; Ahmed, Dastagir; Lili, Guo; Zejian, Wang; Zhe, Wang

    2016-09-01

    This paper studies the learning and generalization performances of pseudo-inverse linear discriminant (PILDs) based on the processing minimum sum-of-squared error (MS(2)E) and the targeting overall classification accuracy (OCA) criterion functions. There is little practicable significance to prove the equivalency between a PILD with the desired outputs in reverse proportion to the number of class samples and an FLD with the totally projected mean thresholds. When the desired outputs of each class are assigned a fixed value, a PILD is partly equal to an FLD. With the customarily desired outputs {1, -1}, a practicable threshold is acquired, which is only related to sample sizes. If the desired outputs of each sample are changeable, a PILD has nothing in common with an FLD. The optimal threshold may thus be singled out from multiple empirical ones related to sizes and distributed regions. Depending upon the processing MS(2)E criteria and the actually algebraic distances, an iterative learning strategy of PILD is proposed, the outstanding advantages of which are with limited epoch, without learning rate and divergent risk. Enormous experimental results for the benchmark datasets have verified that the iterative PILDs with optimal thresholds have good learning and generalization performances, and even reach the top OCAs for some datasets among the existing classifiers.

  15. Time-dependent classification accuracy curve under marker-dependent sampling.

    PubMed

    Zhu, Zhaoyin; Wang, Xiaofei; Saha-Chaudhuri, Paramita; Kosinski, Andrzej S; George, Stephen L

    2016-07-01

    Evaluating the classification accuracy of a candidate biomarker signaling the onset of disease or disease status is essential for medical decision making. A good biomarker would accurately identify the patients who are likely to progress or die at a particular time in the future or who are in urgent need for active treatments. To assess the performance of a candidate biomarker, the receiver operating characteristic (ROC) curve and the area under the ROC curve (AUC) are commonly used. In many cases, the standard simple random sampling (SRS) design used for biomarker validation studies is costly and inefficient. In order to improve the efficiency and reduce the cost of biomarker validation, marker-dependent sampling (MDS) may be used. In a MDS design, the selection of patients to assess true survival time is dependent on the result of a biomarker assay. In this article, we introduce a nonparametric estimator for time-dependent AUC under a MDS design. The consistency and the asymptotic normality of the proposed estimator is established. Simulation shows the unbiasedness of the proposed estimator and a significant efficiency gain of the MDS design over the SRS design. PMID:27119599

  16. Phase noise in pulsed Doppler lidar and limitations on achievable single-shot velocity accuracy

    NASA Technical Reports Server (NTRS)

    Mcnicholl, P.; Alejandro, S.

    1992-01-01

    The smaller sampling volumes afforded by Doppler lidars compared to radars allows for spatial resolutions at and below some sheer and turbulence wind structure scale sizes. This has brought new emphasis on achieving the optimum product of wind velocity and range resolutions. Several recent studies have considered the effects of amplitude noise, reduction algorithms, and possible hardware related signal artifacts on obtainable velocity accuracy. We discuss here the limitation on this accuracy resulting from the incoherent nature and finite temporal extent of backscatter from aerosols. For a lidar return from a hard (or slab) target, the phase of the intermediate frequency (IF) signal is random and the total return energy fluctuates from shot to shot due to speckle; however, the offset from the transmitted frequency is determinable with an accuracy subject only to instrumental effects and the signal to noise ratio (SNR), the noise being determined by the LO power in the shot noise limited regime. This is not the case for a return from a media extending over a range on the order of or greater than the spatial extent of the transmitted pulse, such as from atmospheric aerosols. In this case, the phase of the IF signal will exhibit a temporal random walk like behavior. It will be uncorrelated over times greater than the pulse duration as the transmitted pulse samples non-overlapping volumes of scattering centers. Frequency analysis of the IF signal in a window similar to the transmitted pulse envelope will therefore show shot-to-shot frequency deviations on the order of the inverse pulse duration reflecting the random phase rate variations. Like speckle, these deviations arise from the incoherent nature of the scattering process and diminish if the IF signal is averaged over times greater than a single range resolution cell (here the pulse duration). Apart from limiting the high SNR performance of a Doppler lidar, this shot-to-shot variance in velocity estimates has a

  17. The impact of sampling regime on the accuracy of water quality status classifications under the Water Framework Directive

    NASA Astrophysics Data System (ADS)

    Halliday, Sarah; Wade, Andrew; Skeffington, Richard; Bowes, Mike; Gozzard, Emma; Palmer-Felgate, Elizabeth; Newman, Johnathan; Jarvie, Helen; Loewenthal, Matt

    2014-05-01

    By 2015, EU regulatory agencies have a statutory obligation to meet the EU Water Framework Directive (WFD) target of "good ecological status" in all relevant inland and coastal waters. A significant amount of work is being undertaken to refine and improve the UK WFD water quality targets so that they better relate to the ecological status of a system. In 2013 new phosphorus (P) targets have been set, stipulating required lower mean annual "reactive" P concentrations, and recommendations published for more stringent pH, dissolved oxygen and ammonia targets. Despite this work, there are no guidelines on the sampling regime which should be employed to ensure compliance as part of the WFD classification system. Without guidance on how WFD water quality assessments should be done, regulatory agencies are at risk of misclassifying a system and of failing to identify systems which are ecologically at risk. Water quality is normally evaluated using routine monitoring programmes which use water samples collected, typically, at monthly intervals. However, new technologies are now allowing the collection of high-frequency (sub-daily) measurements of a range of water quality parameters which are revolutionising our understanding of freshwater nutrient cycling dynamics and the processes which control them. High-frequency and weekly water quality datasets for two lowland UK catchments, the River Enborne and The Cut, have been analysed to assess the impact of sampling frequency on the accuracy of WFD status classification. The Enborne is a rural catchment, impacted by agricultural runoff and sewage treatment works (STWs) discharges, and The Cut is a highly urbanised system significantly affected by STW discharges. On the Enborne, total reactive P (TRP) was measured hourly and soluble reactive P (SRP) measured weekly. Under the new WFD targets, although the mean annual P concentrations were similar, 0.173 and 0.136 mg/l-P for TRP and SRP respectively, the two "reactive" P

  18. A Comparative Accuracy Analysis of Classification Methods in Determination of Cultivated Lands with Spot 5 Satellite Imagery

    NASA Astrophysics Data System (ADS)

    kaya, S.; Alganci, U.; Sertel, E.; Ustundag, B.

    2013-12-01

    A Comparative Accuracy Analysis of Classification Methods in Determination of Cultivated Lands with Spot 5 Satellite Imagery Ugur ALGANCI1, Sinasi KAYA1,2, Elif SERTEL1,2,Berk USTUNDAG3 1 ITU, Center for Satellite Communication and Remote Sensing, 34469, Maslak-Istanbul,Turkey 2 ITU, Department of Geomatics, 34469, Maslak-Istanbul, Turkey 3 ITU, Agricultural and Environmental Informatics Research Center,34469, Maslak-Istanbul,Turkey alganci@itu.edu.tr, kayasina@itu.edu.tr, sertele@itu.edu.tr, berk@berk.tc ABSTRACT Cultivated land determination and their area estimation are important tasks for agricultural management. Derived information is mostly used in agricultural policies and precision agriculture, in specifically; yield estimation, irrigation and fertilization management and farmers declaration verification etc. The use of satellite image in crop type identification and area estimate is common for two decades due to its capability of monitoring large areas, rapid data acquisition and spectral response to crop properties. With launch of high and very high spatial resolution optical satellites in the last decade, such kind of analysis have gained importance as they provide information at big scale. With increasing spatial resolution of satellite images, image classification methods to derive the information form them have become important with increase of the spectral heterogeneity within land objects. In this research, pixel based classification with maximum likelihood algorithm and object based classification with nearest neighbor algorithm were applied to 2012 dated 2.5 m resolution SPOT 5 satellite images in order to investigate the accuracy of these methods in determination of cotton and corn planted lands and their area estimation. Study area was selected in Sanliurfa Province located on Southeastern Turkey that contributes to Turkey's agricultural production in a major way. Classification results were compared in terms of crop type identification using

  19. What level of accuracy is achievable for preclinical dose painting studies on a clinical irradiation platform?

    PubMed

    Trani, Daniela; Reniers, Brigitte; Persoon, Lucas; Podesta, Mark; Nalbantov, Georgi; Leijenaar, Ralph T H; Granzier, Marlies; Yaromina, Ala; Dubois, Ludwig; Verhaegen, Frank; Lambin, Philippe

    2015-05-01

    in a rat tumor model on a clinical platform, with a high accuracy achieved in the delivery of complex dose distributions. Our work demonstrates the technical feasibility of this approach and enables future investigations on the therapeutic effect of preclinical dose painting strategies using a state-of-the-art clinical platform.

  20. A comparison of the accuracy of pixel based and object based classifications of integrated optical and LiDAR data

    NASA Astrophysics Data System (ADS)

    Gajda, Agnieszka; Wójtowicz-Nowakowska, Anna

    2013-04-01

    A comparison of the accuracy of pixel based and object based classifications of integrated optical and LiDAR data Land cover maps are generally produced on the basis of high resolution imagery. Recently, LiDAR (Light Detection and Ranging) data have been brought into use in diverse applications including land cover mapping. In this study we attempted to assess the accuracy of land cover classification using both high resolution aerial imagery and LiDAR data (airborne laser scanning, ALS), testing two classification approaches: a pixel-based classification and object-oriented image analysis (OBIA). The study was conducted on three test areas (3 km2 each) in the administrative area of Kraków, Poland, along the course of the Vistula River. They represent three different dominating land cover types of the Vistula River valley. Test site 1 had a semi-natural vegetation, with riparian forests and shrubs, test site 2 represented a densely built-up area, and test site 3 was an industrial site. Point clouds from ALS and ortophotomaps were both captured in November 2007. Point cloud density was on average 16 pt/m2 and it contained additional information about intensity and encoded RGB values. Ortophotomaps had a spatial resolution of 10 cm. From point clouds two raster maps were generated: intensity (1) and (2) normalised Digital Surface Model (nDSM), both with the spatial resolution of 50 cm. To classify the aerial data, a supervised classification approach was selected. Pixel based classification was carried out in ERDAS Imagine software. Ortophotomaps and intensity and nDSM rasters were used in classification. 15 homogenous training areas representing each cover class were chosen. Classified pixels were clumped to avoid salt and pepper effect. Object oriented image object classification was carried out in eCognition software, which implements both the optical and ALS data. Elevation layers (intensity, firs/last reflection, etc.) were used at segmentation stage due to

  1. Recurrent network of perceptrons with three state synapses achieves competitive classification on real inputs

    PubMed Central

    Amit, Yali; Walker, Jacob

    2012-01-01

    We describe an attractor network of binary perceptrons receiving inputs from a retinotopic visual feature layer. Each class is represented by a random subpopulation of the attractor layer, which is turned on in a supervised manner during learning of the feed forward connections. These are discrete three state synapses and are updated based on a simple field dependent Hebbian rule. For testing, the attractor layer is initialized by the feedforward inputs and then undergoes asynchronous random updating until convergence to a stable state. Classification is indicated by the sub-population that is persistently activated. The contribution of this paper is two-fold. This is the first example of competitive classification rates of real data being achieved through recurrent dynamics in the attractor layer, which is only stable if recurrent inhibition is introduced. Second, we demonstrate that employing three state synapses with feedforward inhibition is essential for achieving the competitive classification rates due to the ability to effectively employ both positive and negative informative features. PMID:22737121

  2. Comparison of accuracy of fibrosis degree classifications by liver biopsy and non-invasive tests in chronic hepatitis C

    PubMed Central

    2011-01-01

    Background Non-invasive tests have been constructed and evaluated mainly for binary diagnoses such as significant fibrosis. Recently, detailed fibrosis classifications for several non-invasive tests have been developed, but their accuracy has not been thoroughly evaluated in comparison to liver biopsy, especially in clinical practice and for Fibroscan. Therefore, the main aim of the present study was to evaluate the accuracy of detailed fibrosis classifications available for non-invasive tests and liver biopsy. The secondary aim was to validate these accuracies in independent populations. Methods Four HCV populations provided 2,068 patients with liver biopsy, four different pathologist skill-levels and non-invasive tests. Results were expressed as percentages of correctly classified patients. Results In population #1 including 205 patients and comparing liver biopsy (reference: consensus reading by two experts) and blood tests, Metavir fibrosis (FM) stage accuracy was 64.4% in local pathologists vs. 82.2% (p < 10-3) in single expert pathologist. Significant discrepancy (≥ 2FM vs reference histological result) rates were: Fibrotest: 17.2%, FibroMeter2G: 5.6%, local pathologists: 4.9%, FibroMeter3G: 0.5%, expert pathologist: 0% (p < 10-3). In population #2 including 1,056 patients and comparing blood tests, the discrepancy scores, taking into account the error magnitude, of detailed fibrosis classification were significantly different between FibroMeter2G (0.30 ± 0.55) and FibroMeter3G (0.14 ± 0.37, p < 10-3) or Fibrotest (0.84 ± 0.80, p < 10-3). In population #3 (and #4) including 458 (359) patients and comparing blood tests and Fibroscan, accuracies of detailed fibrosis classification were, respectively: Fibrotest: 42.5% (33.5%), Fibroscan: 64.9% (50.7%), FibroMeter2G: 68.7% (68.2%), FibroMeter3G: 77.1% (83.4%), p < 10-3 (p < 10-3). Significant discrepancy (≥ 2 FM) rates were, respectively: Fibrotest: 21.3% (22.2%), Fibroscan: 12.9% (12.3%), FibroMeter2G: 5

  3. Methods for improving accuracy and extending results beyond periods covered by traditional ground-truth in remote sensing classification of a complex landscape

    NASA Astrophysics Data System (ADS)

    Mueller-Warrant, George W.; Whittaker, Gerald W.; Banowetz, Gary M.; Griffith, Stephen M.; Barnhart, Bradley L.

    2015-06-01

    Successful development of approaches to quantify impacts of diverse landuse and associated agricultural management practices on ecosystem services is frequently limited by lack of historical and contemporary landuse data. We hypothesized that ground truth data from one year could be used to extrapolate previous or future landuse in a complex landscape where cropping systems do not generally change greatly from year to year because the majority of crops are established perennials or the same annual crops grown on the same fields over multiple years. Prior to testing this hypothesis, it was first necessary to classify 57 major landuses in the Willamette Valley of western Oregon from 2005 to 2011 using normal same year ground-truth, elaborating on previously published work and traditional sources such as Cropland Data Layers (CDL) to more fully include minor crops grown in the region. Available remote sensing data included Landsat, MODIS 16-day composites, and National Aerial Imagery Program (NAIP) imagery, all of which were resampled to a common 30 m resolution. The frequent presence of clouds and Landsat7 scan line gaps forced us to conduct of series of separate classifications in each year, which were then merged by choosing whichever classification used the highest number of cloud- and gap-free bands at any given pixel. Procedures adopted to improve accuracy beyond that achieved by maximum likelihood pixel classification included majority-rule reclassification of pixels within 91,442 Common Land Unit (CLU) polygons, smoothing and aggregation of areas outside the CLU polygons, and majority-rule reclassification over time of forest and urban development areas. Final classifications in all seven years separated annually disturbed agriculture, established perennial crops, forest, and urban development from each other at 90 to 95% overall 4-class validation accuracy. In the most successful use of subsequent year ground-truth data to classify prior year landuse, an

  4. Systematic classification of unseeded batch crystallization systems for achievable shape and size analysis

    NASA Astrophysics Data System (ADS)

    Acevedo, David; Nagy, Zoltan K.

    2014-05-01

    The purpose of the current work is to develop a systematic classification scheme for crystallization systems considering simultaneous size and shape variations, and to study the effect of temperature profiles on the achievable final shape of crystals for various crystallization systems. A classification method is proposed based on the simultaneous consideration of the effect of temperature profiles on nucleation and growth rates of two different characteristic crystal dimensions. Hence the approach provides direct indication of the extent in which crystal shape may be controlled for a particular system class by manipulating the supersaturation. A multidimensional population balance model (PBM) was implemented for unseeded crystallization processes of four different compounds. The effect between the nucleation and growth mechanisms on the final aspect ratio (AR) was investigated and it was shown that for nucleation dominated systems the AR is independent of the supersaturation profile. The simulation results confirmed experimentally also show that most crystallization systems tend to achieve an equilibrium shape hence the variation in the aspect ratio that can be achieved by manipulating the supersaturation is limited, in particular when nucleation is also taken into account as a competing phenomenon.

  5. Technical Aspects of Nominal Partitions on Accuracy of Data Mining Classification of Intestinal Microbiota - Comparison between 7 Restriction Enzymes.

    PubMed

    Kobayashi, Toshio; Fujiwara, Kenji

    2014-01-01

    The application of data mining analyses (DM) is effective for the quantitative classification of human intestinal microbiota (HIM). However, there remain various technical problems that must be overcome. This paper deals with the number of nominal partitions (NP) of the target dataset, which is a major technical problem. We used here terminal restriction fragment length polymorphism data, which was obtained from the feces of 92 Japanese men. Data comprised operational taxonomic units (OTUs) and subject smoking and drinking habits, which were effectively classified by two NP (2-NP; Yes or No). Using the same OTU data, 3-NP and 5-NP were examined here and results were obtained, focusing on the accuracies of prediction, and the reliability of the selected OTUs by DM were compared to the former 2-NP. Restriction enzymes for PCR were further affected by the accuracy and were compared with 7 enzymes. There were subjects who possess HIM at the border zones of partitions, and the greater the number of partitions, the lower the obtained DM accuracy. The application of balance nodes boosted and duplicated the data, and was able to improve accuracy. More accurate and reliable DM operations are applicable to the classification of unknown subjects for identifying various characteristics, including disease.

  6. Classification and Accuracy Assessment for Coarse Resolution Mapping within the Great Lakes Basin, USA

    EPA Science Inventory

    This study applied a phenology-based land-cover classification approach across the Laurentian Great Lakes Basin (GLB) using time-series data consisting of 23 Moderate Resolution Imaging Spectroradiometer (MODIS) Normalized Difference Vegetation Index (NDVI) composite images (250 ...

  7. Referential Communication Accuracy of Mother-Child Pairs and Children's Later Scholastic Achievement: A Follow-Up Study.

    ERIC Educational Resources Information Center

    McDevitt, Teresa M.; And Others

    1987-01-01

    The relationship between the referential communication accuracy of mothers and their 4-year-old children and the children's achievement in vocabulary and mathematics at age 12 was examined in 47 American and 44 Japanese mother-child pairs. Positive correlations were found in both cultures. (Author/BN)

  8. The Effects of Individual or Group Guidelines on the Calibration Accuracy and Achievement of High School Biology Students

    ERIC Educational Resources Information Center

    Bol, Linda; Hacker, Douglas J.; Walck, Camilla C.; Nunnery, John A.

    2012-01-01

    A 2 x 2 factorial design was employed in a quasi-experiment to investigate the effects of guidelines in group or individual settings on the calibration accuracy and achievement of 82 high school biology students. Significant main effects indicated that calibration practice with guidelines and practice in group settings increased prediction and…

  9. Effect of radiance-to-reflectance transformation and atmosphere removal on maximum likelihood classification accuracy of high-dimensional remote sensing data

    NASA Technical Reports Server (NTRS)

    Hoffbeck, Joseph P.; Landgrebe, David A.

    1994-01-01

    Many analysis algorithms for high-dimensional remote sensing data require that the remotely sensed radiance spectra be transformed to approximate reflectance to allow comparison with a library of laboratory reflectance spectra. In maximum likelihood classification, however, the remotely sensed spectra are compared to training samples, thus a transformation to reflectance may or may not be helpful. The effect of several radiance-to-reflectance transformations on maximum likelihood classification accuracy is investigated in this paper. We show that the empirical line approach, LOWTRAN7, flat-field correction, single spectrum method, and internal average reflectance are all non-singular affine transformations, and that non-singular affine transformations have no effect on discriminant analysis feature extraction and maximum likelihood classification accuracy. (An affine transformation is a linear transformation with an optional offset.) Since the Atmosphere Removal Program (ATREM) and the log residue method are not affine transformations, experiments with Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) data were conducted to determine the effect of these transformations on maximum likelihood classification accuracy. The average classification accuracy of the data transformed by ATREM and the log residue method was slightly less than the accuracy of the original radiance data. Since the radiance-to-reflectance transformations allow direct comparison of remotely sensed spectra with laboratory reflectance spectra, they can be quite useful in labeling the training samples required by maximum likelihood classification, but these transformations have only a slight effect or no effect at all on discriminant analysis and maximum likelihood classification accuracy.

  10. Changes in classification accuracy due to varying Thematic Mapper and multispectral scanner spatial, spectral, and radiometric resolution

    NASA Technical Reports Server (NTRS)

    Acevedo, W.; Buis, J. S.; Wrigley, R. C.

    1985-01-01

    The present paper provides the results of a factorial experiment designed to study the classification differences resulting from varying TM and MSS sensor resolution. Eight simulated data sets of various TM and MSS spatial, spectral, and radiometric resolutions were generated on the basis of Daedalus aircraft scanner data. It is pointed out that the current study provides more precise results than previous work, because more exact methods of data simulation with regard to the three factors were emphasized. Two methods of analysis are considered in the paper. To improve on earlier studies, efforts were made to collect an extensive amount of ground reference data. The summaries of classification accuracies for the training sites in the factorial analysis are presented in a table.

  11. IMPACTS OF PATCH SIZE AND LAND COVER HETEROGENEITY ON THEMATIC IMAGE CLASSIFICATION ACCURACY

    EPA Science Inventory


    Landscape characteristics such as small patch size and land cover heterogeneity have been hypothesized to increase the likelihood of miss-classifying pixels during thematic image classification. However, there has been a lack of empirical evidence to support these hypotheses,...

  12. Design considerations for achieving high accuracy with the SHOALS bathymetric lidar system

    NASA Astrophysics Data System (ADS)

    Guenther, Gary C.; Thomas, Robert W. L.; LaRocque, Paul E.

    1996-11-01

    The ultimate accuracy of depths from an airborne laser hydrography system depends both on careful hardware design aimed at producing the best possible accuracy and precision of recorded data, along with insensitivity to environmental effects, and on post-flight data processing software which corrects for a number of unavoidable biases and provides for flexible operator interaction to handle special cases. The generic procedure for obtaining a depth from an airborne lidar pulse involves measurement of the time between the surface return and the bottom return. In practice, because both of these return times are biased due to a number of environmental and hardware effects, it is necessary to apply various correctors in order to obtain depth estimates which are sufficiently accurate to meet International Hydrographic Office standards. Potential false targets, also of both environmental and hardware origin, must be discriminated, and wave heights must be removed. It is important to have a depth confidence value matched to accuracy and to have warnings about or automatic deletion of pulses with questionable characteristics. Techniques, procedures, and algorithms developed for the SHOALS systems are detailed here.

  13. Multispectral imaging burn wound tissue classification system: a comparison of test accuracies between several common machine learning algorithms

    NASA Astrophysics Data System (ADS)

    Squiers, John J.; Li, Weizhi; King, Darlene R.; Mo, Weirong; Zhang, Xu; Lu, Yang; Sellke, Eric W.; Fan, Wensheng; DiMaio, J. Michael; Thatcher, Jeffrey E.

    2016-03-01

    The clinical judgment of expert burn surgeons is currently the standard on which diagnostic and therapeutic decisionmaking regarding burn injuries is based. Multispectral imaging (MSI) has the potential to increase the accuracy of burn depth assessment and the intraoperative identification of viable wound bed during surgical debridement of burn injuries. A highly accurate classification model must be developed using machine-learning techniques in order to translate MSI data into clinically-relevant information. An animal burn model was developed to build an MSI training database and to study the burn tissue classification ability of several models trained via common machine-learning algorithms. The algorithms tested, from least to most complex, were: K-nearest neighbors (KNN), decision tree (DT), linear discriminant analysis (LDA), weighted linear discriminant analysis (W-LDA), quadratic discriminant analysis (QDA), ensemble linear discriminant analysis (EN-LDA), ensemble K-nearest neighbors (EN-KNN), and ensemble decision tree (EN-DT). After the ground-truth database of six tissue types (healthy skin, wound bed, blood, hyperemia, partial injury, full injury) was generated by histopathological analysis, we used 10-fold cross validation to compare the algorithms' performances based on their accuracies in classifying data against the ground truth, and each algorithm was tested 100 times. The mean test accuracy of the algorithms were KNN 68.3%, DT 61.5%, LDA 70.5%, W-LDA 68.1%, QDA 68.9%, EN-LDA 56.8%, EN-KNN 49.7%, and EN-DT 36.5%. LDA had the highest test accuracy, reflecting the bias-variance tradeoff over the range of complexities inherent to the algorithms tested. Several algorithms were able to match the current standard in burn tissue classification, the clinical judgment of expert burn surgeons. These results will guide further development of an MSI burn tissue classification system. Given that there are few surgeons and facilities specializing in burn care

  14. Classification accuracy of algorithms for blood chemistry data for three aquaculture-affected marine fish species.

    PubMed

    Coz-Rakovac, R; Topic Popovic, N; Smuc, T; Strunjak-Perovic, I; Jadan, M

    2009-11-01

    The objective of this study was determination and discrimination of biochemical data among three aquaculture-affected marine fish species (sea bass, Dicentrarchus labrax; sea bream, Sparus aurata L., and mullet, Mugil spp.) based on machine-learning methods. The approach relying on machine-learning methods gives more usable classification solutions and provides better insight into the collected data. So far, these new methods have been applied to the problem of discrimination of blood chemistry data with respect to season and feed of a single species. This is the first time these classification algorithms have been used as a framework for rapid differentiation among three fish species. Among the machine-learning methods used, decision trees provided the clearest model, which correctly classified 210 samples or 85.71%, and incorrectly classified 35 samples or 14.29% and clearly identified three investigated species from their biochemical traits.

  15. Absolute radiometric calibration of Als intensity data: effects on accuracy and target classification.

    PubMed

    Kaasalainen, Sanna; Pyysalo, Ulla; Krooks, Anssi; Vain, Ants; Kukko, Antero; Hyyppä, Juha; Kaasalainen, Mikko

    2011-01-01

    Radiometric calibration of airborne laser scanning (ALS) intensity data aims at retrieving a value related to the target scattering properties, which is independent on the instrument or flight parameters. The aim of a calibration procedure is also to be able to compare results from different flights and instruments, but practical applications are sparsely available, and the performance of calibration methods for this purpose needs to be further assessed. We have studied the radiometric calibration with data from three separate flights and two different instruments using external calibration targets. We find that the intensity data from different flights and instruments can be compared to each other only after a radiometric calibration process using separate calibration targets carefully selected for each flight. The calibration is also necessary for target classification purposes, such as separating vegetation from sand using intensity data from different flights. The classification results are meaningful only for calibrated intensity data.

  16. Improving ECG Classification Accuracy Using an Ensemble of Neural Network Modules

    PubMed Central

    Javadi, Mehrdad; Ebrahimpour, Reza; Sajedin, Atena; Faridi, Soheil; Zakernejad, Shokoufeh

    2011-01-01

    This paper illustrates the use of a combined neural network model based on Stacked Generalization method for classification of electrocardiogram (ECG) beats. In conventional Stacked Generalization method, the combiner learns to map the base classifiers' outputs to the target data. We claim adding the input pattern to the base classifiers' outputs helps the combiner to obtain knowledge about the input space and as the result, performs better on the same task. Experimental results support our claim that the additional knowledge according to the input space, improves the performance of the proposed method which is called Modified Stacked Generalization. In particular, for classification of 14966 ECG beats that were not previously seen during training phase, the Modified Stacked Generalization method reduced the error rate for 12.41% in comparison with the best of ten popular classifier fusion methods including Max, Min, Average, Product, Majority Voting, Borda Count, Decision Templates, Weighted Averaging based on Particle Swarm Optimization and Stacked Generalization. PMID:22046232

  17. Absolute radiometric calibration of Als intensity data: effects on accuracy and target classification.

    PubMed

    Kaasalainen, Sanna; Pyysalo, Ulla; Krooks, Anssi; Vain, Ants; Kukko, Antero; Hyyppä, Juha; Kaasalainen, Mikko

    2011-01-01

    Radiometric calibration of airborne laser scanning (ALS) intensity data aims at retrieving a value related to the target scattering properties, which is independent on the instrument or flight parameters. The aim of a calibration procedure is also to be able to compare results from different flights and instruments, but practical applications are sparsely available, and the performance of calibration methods for this purpose needs to be further assessed. We have studied the radiometric calibration with data from three separate flights and two different instruments using external calibration targets. We find that the intensity data from different flights and instruments can be compared to each other only after a radiometric calibration process using separate calibration targets carefully selected for each flight. The calibration is also necessary for target classification purposes, such as separating vegetation from sand using intensity data from different flights. The classification results are meaningful only for calibrated intensity data. PMID:22346660

  18. You are so beautiful... to me: seeing beyond biases and achieving accuracy in romantic relationships.

    PubMed

    Solomon, Brittany C; Vazire, Simine

    2014-09-01

    Do romantic partners see each other realistically, or do they have overly positive perceptions of each other? Research has shown that realism and positivity co-exist in romantic partners' perceptions (Boyes & Fletcher, 2007). The current study takes a novel approach to explaining this seemingly paradoxical effect when it comes to physical attractiveness--a highly evaluative trait that is especially relevant to romantic relationships. Specifically, we argue that people are aware that others do not see their partners as positively as they do. Using both mean differences and correlational approaches, we test the hypothesis that despite their own biased and idiosyncratic perceptions, people have 2 types of partner-knowledge: insight into how their partners see themselves (i.e., identity accuracy) and insight into how others see their partners (i.e., reputation accuracy). Our results suggest that romantic partners have some awareness of each other's identity and reputation for physical attractiveness, supporting theories that couple members' perceptions are driven by motives to fulfill both esteem- and epistemic-related needs (i.e., to see their partners positively and realistically). PMID:25133729

  19. Multinomial tree models for assessing the status of the reference in studies of the accuracy of tools for binary classification

    PubMed Central

    Botella, Juan; Huang, Huiling; Suero, Manuel

    2013-01-01

    Studies that evaluate the accuracy of binary classification tools are needed. Such studies provide 2 × 2 cross-classifications of test outcomes and the categories according to an unquestionable reference (or gold standard). However, sometimes a suboptimal reliability reference is employed. Several methods have been proposed to deal with studies where the observations are cross-classified with an imperfect reference. These methods require that the status of the reference, as a gold standard or as an imperfect reference, is known. In this paper a procedure for determining whether it is appropriate to maintain the assumption that the reference is a gold standard or an imperfect reference, is proposed. This procedure fits two nested multinomial tree models, and assesses and compares their absolute and incremental fit. Its implementation requires the availability of the results of several independent studies. These should be carried out using similar designs to provide frequencies of cross-classification between a test and the reference under investigation. The procedure is applied in two examples with real data. PMID:24106484

  20. Multinomial tree models for assessing the status of the reference in studies of the accuracy of tools for binary classification.

    PubMed

    Botella, Juan; Huang, Huiling; Suero, Manuel

    2013-01-01

    Studies that evaluate the accuracy of binary classification tools are needed. Such studies provide 2 × 2 cross-classifications of test outcomes and the categories according to an unquestionable reference (or gold standard). However, sometimes a suboptimal reliability reference is employed. Several methods have been proposed to deal with studies where the observations are cross-classified with an imperfect reference. These methods require that the status of the reference, as a gold standard or as an imperfect reference, is known. In this paper a procedure for determining whether it is appropriate to maintain the assumption that the reference is a gold standard or an imperfect reference, is proposed. This procedure fits two nested multinomial tree models, and assesses and compares their absolute and incremental fit. Its implementation requires the availability of the results of several independent studies. These should be carried out using similar designs to provide frequencies of cross-classification between a test and the reference under investigation. The procedure is applied in two examples with real data.

  1. Achieving sub-pixel geolocation accuracy in support of MODIS land science

    USGS Publications Warehouse

    Wolfe, R.E.; Nishihama, M.; Fleig, A.J.; Kuyper, J.A.; Roy, D.P.; Storey, J.C.; Patt, F.S.

    2002-01-01

    The Moderate Resolution Imaging Spectroradiometer (MODIS) was launched in December 1999 on the polar orbiting Terra spacecraft and since February 2000 has been acquiring daily global data in 36 spectral bands-29 with 1 km, five with 500 m, and two with 250 m nadir pixel dimensions. The Terra satellite has on-board exterior orientation (position and attitude) measurement systems designed to enable geolocation of MODIS data to approximately 150 m (1??) at nadir. A global network of ground control points is being used to determine biases and trends in the sensor orientation. Biases have been removed by updating models of the spacecraft and instrument orientation in the MODIS geolocation software several times since launch and have improved the MODIS geolocation to approximately 50 m (1??) at nadir. This paper overviews the geolocation approach, summarizes the first year of geolocation analysis, and overviews future work. The approach allows an operational characterization of the MODIS geolocation errors and enables individual MODIS observations to be geolocated to the sub-pixel accuracies required for terrestrial global change applications. ?? 2002 Elsevier Science Inc. All rights reserved.

  2. a Method to Achieve Large Volume, High Accuracy Photogrammetric Measurements Through the Use of AN Actively Deformable Sensor Mounting Platform

    NASA Astrophysics Data System (ADS)

    Sargeant, B.; Robson, S.; Szigeti, E.; Richardson, P.; El-Nounu, A.; Rafla, M.

    2016-06-01

    When using any optical measurement system one important factor to consider is the placement of the sensors in relation to the workpiece being measured. When making decisions on sensor placement compromises are necessary in selecting the best placement based on the shape and size of the object of interest and the desired resolution and accuracy. One such compromise is in the distance the sensors are placed from the measurement surface, where a smaller distance gives a higher spatial resolution and local accuracy and a greater distance reduces the number of measurements necessary to cover a large area reducing the build-up of errors between measurements and increasing global accuracy. This paper proposes a photogrammetric approach whereby a number of sensors on a continuously flexible mobile platform are used to obtain local measurements while the position of the sensors is determined by a 6DoF tracking solution and the results combined to give a single set of measurement data within a continuous global coordinate system. The ability of this approach to achieve both high accuracy measurement and give results over a large volume is then tested and areas of weakness to be improved upon are identified.

  3. Assessing the accuracy of the International Classification of Diseases codes to identify abusive head trauma: a feasibility study

    PubMed Central

    Berger, Rachel P; Parks, Sharyn; Fromkin, Janet; Rubin, Pamela; Pecora, Peter J

    2016-01-01

    Objective To assess the accuracy of an International Classification of Diseases (ICD) code-based operational case definition for abusive head trauma (AHT). Methods Subjects were children <5 years of age evaluated for AHT by a hospital-based Child Protection Team (CPT) at a tertiary care paediatric hospital with a completely electronic medical record (EMR) system. Subjects were designated as non-AHT traumatic brain injury (TBI) or AHT based on whether the CPT determined that the injuries were due to AHT. The sensitivity and specificity of the ICD-based definition were calculated. Results There were 223 children evaluated for AHT: 117 AHT and 106 non-AHT TBI. The sensitivity and specificity of the ICD-based operational case definition were 92% (95% CI 85.8 to 96.2) and 96% (95% CI 92.3 to 99.7), respectively. All errors in sensitivity and three of the four specificity errors were due to coder error; one specificity error was a physician error. Conclusions In a paediatric tertiary care hospital with an EMR system, the accuracy of an ICD-based case definition for AHT was high. Additional studies are needed to assess the accuracy of this definition in all types of hospitals in which children with AHT are cared for. PMID:24167034

  4. Clinical decision support systems for improving diagnostic accuracy and achieving precision medicine.

    PubMed

    Castaneda, Christian; Nalley, Kip; Mannion, Ciaran; Bhattacharyya, Pritish; Blake, Patrick; Pecora, Andrew; Goy, Andre; Suh, K Stephen

    2015-01-01

    As research laboratories and clinics collaborate to achieve precision medicine, both communities are required to understand mandated electronic health/medical record (EHR/EMR) initiatives that will be fully implemented in all clinics in the United States by 2015. Stakeholders will need to evaluate current record keeping practices and optimize and standardize methodologies to capture nearly all information in digital format. Collaborative efforts from academic and industry sectors are crucial to achieving higher efficacy in patient care while minimizing costs. Currently existing digitized data and information are present in multiple formats and are largely unstructured. In the absence of a universally accepted management system, departments and institutions continue to generate silos of information. As a result, invaluable and newly discovered knowledge is difficult to access. To accelerate biomedical research and reduce healthcare costs, clinical and bioinformatics systems must employ common data elements to create structured annotation forms enabling laboratories and clinics to capture sharable data in real time. Conversion of these datasets to knowable information should be a routine institutionalized process. New scientific knowledge and clinical discoveries can be shared via integrated knowledge environments defined by flexible data models and extensive use of standards, ontologies, vocabularies, and thesauri. In the clinical setting, aggregated knowledge must be displayed in user-friendly formats so that physicians, non-technical laboratory personnel, nurses, data/research coordinators, and end-users can enter data, access information, and understand the output. The effort to connect astronomical numbers of data points, including '-omics'-based molecular data, individual genome sequences, experimental data, patient clinical phenotypes, and follow-up data is a monumental task. Roadblocks to this vision of integration and interoperability include ethical, legal

  5. IMPROVING THE ACCURACY OF HISTORIC SATELLITE IMAGE CLASSIFICATION BY COMBINING LOW-RESOLUTION MULTISPECTRAL DATA WITH HIGH-RESOLUTION PANCHROMATIC DATA

    SciTech Connect

    Getman, Daniel J

    2008-01-01

    Many attempts to observe changes in terrestrial systems over time would be significantly enhanced if it were possible to improve the accuracy of classifications of low-resolution historic satellite data. In an effort to examine improving the accuracy of historic satellite image classification by combining satellite and air photo data, two experiments were undertaken in which low-resolution multispectral data and high-resolution panchromatic data were combined and then classified using the ECHO spectral-spatial image classification algorithm and the Maximum Likelihood technique. The multispectral data consisted of 6 multispectral channels (30-meter pixel resolution) from Landsat 7. These data were augmented with panchromatic data (15m pixel resolution) from Landsat 7 in the first experiment, and with a mosaic of digital aerial photography (1m pixel resolution) in the second. The addition of the Landsat 7 panchromatic data provided a significant improvement in the accuracy of classifications made using the ECHO algorithm. Although the inclusion of aerial photography provided an improvement in accuracy, this improvement was only statistically significant at a 40-60% level. These results suggest that once error levels associated with combining aerial photography and multispectral satellite data are reduced, this approach has the potential to significantly enhance the precision and accuracy of classifications made using historic remotely sensed data, as a way to extend the time range of efforts to track temporal changes in terrestrial systems.

  6. Basic visual dysfunction allows classification of patients with schizophrenia with exceptional accuracy.

    PubMed

    González-Hernández, J A; Pita-Alcorta, C; Padrón, A; Finalé, A; Galán, L; Martínez, E; Díaz-Comas, L; Samper-González, J A; Lencer, R; Marot, M

    2014-10-01

    Basic visual dysfunctions are commonly reported in schizophrenia; however their value as diagnostic tools remains uncertain. This study reports a novel electrophysiological approach using checkerboard visual evoked potentials (VEP). Sources of spectral resolution VEP-components C1, P1 and N1 were estimated by LORETA, and the band-effects (BSE) on these estimated sources were explored in each subject. BSEs were Z-transformed for each component and relationships with clinical variables were assessed. Clinical effects were evaluated by ROC-curves and predictive values. Forty-eight patients with schizophrenia (SZ) and 55 healthy controls participated in the study. For each of the 48 patients, the three VEP components were localized to both dorsal and ventral brain areas and also deviated from a normal distribution. P1 and N1 deviations were independent of treatment, illness chronicity or gender. Results from LORETA also suggest that deficits in thalamus, posterior cingulum, precuneus, superior parietal and medial occipitotemporal areas were associated with symptom severity. While positive symptoms were more strongly related to sensory processing deficits (P1), negative symptoms were more strongly related to perceptual processing dysfunction (N1). Clinical validation revealed positive and negative predictive values for correctly classifying SZ of 100% and 77%, respectively. Classification in an additional independent sample of 30 SZ corroborated these results. In summary, this novel approach revealed basic visual dysfunctions in all patients with schizophrenia, suggesting these visual dysfunctions represent a promising candidate as a biomarker for schizophrenia.

  7. Impact of the accuracy of automatic segmentation of cell nuclei clusters on classification of thyroid follicular lesions.

    PubMed

    Jung, Chanho; Kim, Changick

    2014-08-01

    Automatic segmentation of cell nuclei clusters is a key building block in systems for quantitative analysis of microscopy cell images. For that reason, it has received a great attention over the last decade, and diverse automatic approaches to segment clustered nuclei with varying levels of performance under different test conditions have been proposed in literature. To the best of our knowledge, however, so far there is no comparative study on the methods. This study is a first attempt to fill this research gap. More precisely, the purpose of this study is to present an objective performance comparison of existing state-of-the-art segmentation methods. Particularly, the impact of their accuracy on classification of thyroid follicular lesions is also investigated "quantitatively" under the same experimental condition, to evaluate the applicability of the methods. Thirteen different segmentation approaches are compared in terms of not only errors in nuclei segmentation and delineation, but also their impact on the performance of system to classify thyroid follicular lesions using different metrics (e.g., diagnostic accuracy, sensitivity, specificity, etc.). Extensive experiments have been conducted on a total of 204 digitized thyroid biopsy specimens. Our study demonstrates that significant diagnostic errors can be avoided using more advanced segmentation approaches. We believe that this comprehensive comparative study serves as a reference point and guide for developers and practitioners in choosing an appropriate automatic segmentation technique adopted for building automated systems for specifically classifying follicular thyroid lesions.

  8. From genus to phylum: large-subunit and internal transcribed spacer rRNA operon regions show similar classification accuracies influenced by database composition.

    PubMed

    Porras-Alfaro, Andrea; Liu, Kuan-Liang; Kuske, Cheryl R; Xie, Gary

    2014-02-01

    We compared the classification accuracy of two sections of the fungal internal transcribed spacer (ITS) region, individually and combined, and the 5' section (about 600 bp) of the large-subunit rRNA (LSU), using a naive Bayesian classifier and BLASTN. A hand-curated ITS-LSU training set of 1,091 sequences and a larger training set of 8,967 ITS region sequences were used. Of the factors evaluated, database composition and quality had the largest effect on classification accuracy, followed by fragment size and use of a bootstrap cutoff to improve classification confidence. The naive Bayesian classifier and BLASTN gave similar results at higher taxonomic levels, but the classifier was faster and more accurate at the genus level when a bootstrap cutoff was used. All of the ITS and LSU sections performed well (>97.7% accuracy) at higher taxonomic ranks from kingdom to family, and differences between them were small at the genus level (within 0.66 to 1.23%). When full-length sequence sections were used, the LSU outperformed the ITS1 and ITS2 fragments at the genus level, but the ITS1 and ITS2 showed higher accuracy when smaller fragment sizes of the same length and a 50% bootstrap cutoff were used. In a comparison using the larger ITS training set, ITS1 and ITS2 had very similar accuracy classification for fragments between 100 and 200 bp. Collectively, the results show that any of the ITS or LSU sections we tested provided comparable classification accuracy to the genus level and underscore the need for larger and more diverse classification training sets.

  9. Accuracy and Classification of River Form and Extent from Remote Observations in Support of the SWOT Satellite Mission

    NASA Astrophysics Data System (ADS)

    Pavelsky, T. M.

    2010-12-01

    Detection of surface water extent is one of the earliest scientific applications of satellite-based remote sensing. Numerous techniques have been developed to extract inundation extent from both optical and microwave imagery and by combining measurements from multiple sensors of both varieties. More recently, methods for extraction of sub-pixel information on inundation extent have been introduced. The application of existing methods, or the development of new ones, will be particularly important to the successful derivation of storage change (dS/dt) and discharge (Q) from measurements provided by the proposed Surface Water and Ocean Topography (SWOT) satellite mission. Traditional methods of discharge calculation depend on accurate measurements of cross-sectional area, which is often estimated from river width. However, these methods are generally optimized for the case of a single-channel river with a relatively simple bathymetric profile. By contrast, SWOT seeks to measure discharge in all types of rivers, including highly braided channels prevalent in high-latitude and dryland rivers. However, discharge accuracy and even the method of estimating discharge may vary depending on river form. As such, it is essential to understand the probably accuracies of SWOT-derived width and inundation extent measurements and to characterize the different types of rivers that SWOT will image. Here, the relevance to SWOT of existing remote sensing-based methods of inundation extent detection will be explored, with an eye towards understanding likely SWOT accuracies and their impact on discharge estimates. In addition, methods of river reach classification will be examined. In particular, we will explore classifications based on channel width, the mean number of channels in a reach, and the relative importance of variations in water surface width and height to discharge measurement in different fluvial environments. The relative importance of width and height will be assessed

  10. Peaks, plateaus, numerical instabilities, and achievable accuracy in Galerkin and norm minimizing procedures for solving Ax=b

    SciTech Connect

    Cullum, J.

    1994-12-31

    Plots of the residual norms generated by Galerkin procedures for solving Ax = b often exhibit strings of irregular peaks. At seemingly erratic stages in the iterations, peaks appear in the residual norm plot, intervals of iterations over which the norms initially increase and then decrease. Plots of the residual norms generated by related norm minimizing procedures often exhibit long plateaus, sequences of iterations over which reductions in the size of the residual norm are unacceptably small. In an earlier paper the author discussed and derived relationships between such peaks and plateaus within corresponding Galerkin/Norm Minimizing pairs of such methods. In this paper, through a set of numerical experiments, the author examines connections between peaks, plateaus, numerical instabilities, and the achievable accuracy for such pairs of iterative methods. Three pairs of methods, GMRES/Arnoldi, QMR/BCG, and two bidiagonalization methods are studied.

  11. Classification

    ERIC Educational Resources Information Center

    Clary, Renee; Wandersee, James

    2013-01-01

    In this article, Renee Clary and James Wandersee describe the beginnings of "Classification," which lies at the very heart of science and depends upon pattern recognition. Clary and Wandersee approach patterns by first telling the story of the "Linnaean classification system," introduced by Carl Linnacus (1707-1778), who is…

  12. Studying the Effect of Adaptive Momentum in Improving the Accuracy of Gradient Descent Back Propagation Algorithm on Classification Problems

    NASA Astrophysics Data System (ADS)

    Rehman, Muhammad Zubair; Nawi, Nazri Mohd.

    Despite being widely used in the practical problems around the world, Gradient Descent Back-propagation algorithm comes with problems like slow convergence and convergence to local minima. Previous researchers have suggested certain modifications to improve the convergence in gradient Descent Back-propagation algorithm such as careful selection of input weights and biases, learning rate, momentum, network topology, activation function and value for 'gain' in the activation function. This research proposed an algorithm for improving the working performance of back-propagation algorithm which is 'Gradient Descent with Adaptive Momentum (GDAM)' by keeping the gain value fixed during all network trials. The performance of GDAM is compared with 'Gradient Descent with fixed Momentum (GDM)' and 'Gradient Descent Method with Adaptive Gain (GDM-AG)'. The learning rate is fixed to 0.4 and maximum epochs are set to 3000 while sigmoid activation function is used for the experimentation. The results show that GDAM is a better approach than previous methods with an accuracy ratio of 1.0 for classification problems like Wine Quality, Mushroom and Thyroid disease.

  13. Classification accuracy of a single tri-axial accelerometer for training background and experience level in runners.

    PubMed

    Kobsar, Dylan; Osis, Sean T; Hettinga, Blayne A; Ferber, Reed

    2014-07-18

    Accelerometers are increasingly used tools for gait analysis, but there remains a lack of research on their application to running and their ability to classify running patterns. The purpose of this study was to conduct an exploratory examination into the capability of a tri-axial accelerometer to classify runners of different training backgrounds and experience levels, according to their 3-dimensional (3D) accelerometer data patterns. Training background was examined with 14 competitive soccer players and 12 experienced marathon runners, and experience level was examined with 16 first-time and the same 12 experienced marathon runners. Discrete variables were extracted from 3D accelerations during a short run using root mean square, wavelet transformation, and autocorrelation procedures. A principal component analysis (PCA) was conducted on all variables, including gait speed to account for covariance. Eight PCs were retained, explaining 88% of the variance in the data. A stepwise discriminant analysis of PCs was used to determine the binary classification accuracy for training background and experience level, with and without the PC of Speed. With Speed, the accelerometer correctly classified 96% of runners for both training background and experience level. Without Speed, the accelerometer correctly classified 85% of runners based on training background, but only 68% based on experience level. These findings suggest that the accelerometer is effective in classifying athletes of different training backgrounds, but is less effective for classifying runners of different experience levels where gait speed is the primary discriminator. PMID:24837221

  14. Influence of multi-source and multi-temporal remotely sensed and ancillary data on the accuracy of random forest classification of wetlands in northern Minnesota

    USGS Publications Warehouse

    Corcoran, Jennifer M.; Knight, Joseph F.; Gallant, Alisa L.

    2013-01-01

    Wetland mapping at the landscape scale using remotely sensed data requires both affordable data and an efficient accurate classification method. Random forest classification offers several advantages over traditional land cover classification techniques, including a bootstrapping technique to generate robust estimations of outliers in the training data, as well as the capability of measuring classification confidence. Though the random forest classifier can generate complex decision trees with a multitude of input data and still not run a high risk of over fitting, there is a great need to reduce computational and operational costs by including only key input data sets without sacrificing a significant level of accuracy. Our main questions for this study site in Northern Minnesota were: (1) how does classification accuracy and confidence of mapping wetlands compare using different remote sensing platforms and sets of input data; (2) what are the key input variables for accurate differentiation of upland, water, and wetlands, including wetland type; and (3) which datasets and seasonal imagery yield the best accuracy for wetland classification. Our results show the key input variables include terrain (elevation and curvature) and soils descriptors (hydric), along with an assortment of remotely sensed data collected in the spring (satellite visible, near infrared, and thermal bands; satellite normalized vegetation index and Tasseled Cap greenness and wetness; and horizontal-horizontal (HH) and horizontal-vertical (HV) polarization using L-band satellite radar). We undertook this exploratory analysis to inform decisions by natural resource managers charged with monitoring wetland ecosystems and to aid in designing a system for consistent operational mapping of wetlands across landscapes similar to those found in Northern Minnesota.

  15. Cognitive Processing Profiles of School-Age Children Who Meet Low-Achievement, IQ-Discrepancy, or Dual Criteria for Underachievement in Oral Reading Accuracy

    ERIC Educational Resources Information Center

    Van Santen, Frank W.

    2012-01-01

    The purpose of this study was to compare the cognitive processing profiles of school-age children (ages 7 to 17) who met criteria for underachievement in oral reading accuracy based on three different methods: 1) use of a regression-based IQ-achievement discrepancy only (REGonly), 2) use of a low-achievement cutoff only (LAonly), and 3) use of a…

  16. Research on the classification result and accuracy of building windows in high resolution satellite images: take the typical rural buildings in Guangxi, China, as an example

    NASA Astrophysics Data System (ADS)

    Li, Baishou; Gao, Yujiu

    2015-12-01

    The information extracted from the high spatial resolution remote sensing images has become one of the important data sources of the GIS large scale spatial database updating. The realization of the building information monitoring using the high resolution remote sensing, building small scale information extracting and its quality analyzing has become an important precondition for the applying of the high-resolution satellite image information, because of the large amount of regional high spatial resolution satellite image data. In this paper, a clustering segmentation classification evaluation method for the high resolution satellite images of the typical rural buildings is proposed based on the traditional KMeans clustering algorithm. The factors of separability and building density were used for describing image classification characteristics of clustering window. The sensitivity of the factors influenced the clustering result was studied from the perspective of the separability between high image itself target and background spectrum. This study showed that the number of the sample contents is the important influencing factor to the clustering accuracy and performance, the pixel ratio of the objects in images and the separation factor can be used to determine the specific impact of cluster-window subsets on the clustering accuracy, and the count of window target pixels (Nw) does not alone affect clustering accuracy. The result can provide effective research reference for the quality assessment of the segmentation and classification of high spatial resolution remote sensing images.

  17. Mapping Crop Patterns in Central US Agricultural Systems from 2000 to 2014 Based on Landsat Data: To What Degree Does Fusing MODIS Data Improve Classification Accuracies?

    NASA Astrophysics Data System (ADS)

    Zhu, L.; Radeloff, V.; Ives, A. R.; Barton, B.

    2015-12-01

    Deriving crop pattern with high accuracy is of great importance for characterizing landscape diversity, which affects the resilience of food webs in agricultural systems in the face of climatic and land cover changes. Landsat sensors were originally designed to monitor agricultural areas, and both radiometric and spatial resolution are optimized for monitoring large agricultural fields. Unfortunately, few clear Landsat images per year are available, which has limited the use of Landsat for making crop classification, and this situation is worse in cloudy areas of the Earth. Meanwhile, the MODerate Resolution Imaging Spectroradiometer (MODIS) data has better temporal resolution but cannot capture fine spatial heterogeneity of agricultural systems. Our question was to what extent fusing imagery from both sensors could improve crop classifications. We utilized the Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) algorithm to simulate Landsat-like images at MODIS temporal resolution. Based on Random Forests (RF) classifier, we tested whether and by what degree crop maps from 2000 to 2014 of the Arlington Agricultural Research Station (Wisconsin, USA) were improved by integrating available clear Landsat images each year with synthetic images. We predicted that the degree to which classification accuracy can be improved by incorporating synthetic imagery depends on the number and acquisition time of clear Landsat images. Moreover, multi-season data are essential for mapping crop types by capturing their phenological dynamics, and STARFM-simulated images can be used to compensate for missing Landsat observations. Our study is helpful for eliminating the limits of the use of Landsat data in mapping crop patterns, and can provide a benchmark of accuracy when choosing STARFM-simulated images to make crop classification at broader scales.

  18. Classification

    NASA Technical Reports Server (NTRS)

    Oza, Nikunj C.

    2011-01-01

    A supervised learning task involves constructing a mapping from input data (normally described by several features) to the appropriate outputs. Within supervised learning, one type of task is a classification learning task, in which each output is one or more classes to which the input belongs. In supervised learning, a set of training examples---examples with known output values---is used by a learning algorithm to generate a model. This model is intended to approximate the mapping between the inputs and outputs. This model can be used to generate predicted outputs for inputs that have not been seen before. For example, we may have data consisting of observations of sunspots. In a classification learning task, our goal may be to learn to classify sunspots into one of several types. Each example may correspond to one candidate sunspot with various measurements or just an image. A learning algorithm would use the supplied examples to generate a model that approximates the mapping between each supplied set of measurements and the type of sunspot. This model can then be used to classify previously unseen sunspots based on the candidate's measurements. This chapter discusses methods to perform machine learning, with examples involving astronomy.

  19. Evaluating IRT- and CTT-Based Methods of Estimating Classification Consistency and Accuracy Indices from Single Administrations

    ERIC Educational Resources Information Center

    Deng, Nina

    2011-01-01

    Three decision consistency and accuracy (DC/DA) methods, the Livingston and Lewis (LL) method, LEE method, and the Hambleton and Han (HH) method, were evaluated. The purposes of the study were: (1) to evaluate the accuracy and robustness of these methods, especially when their assumptions were not well satisfied, (2) to investigate the "true"…

  20. The Effects of Q-Matrix Misspecification on Parameter Estimates and Classification Accuracy in the DINA Model

    ERIC Educational Resources Information Center

    Rupp, Andre A.; Templin, Jonathan

    2008-01-01

    This article reports a study that investigated the effects of Q-matrix misspecifications on parameter estimates and misclassification rates for the deterministic-input, noisy "and" gate (DINA) model, which is a restricted latent class model for multiple classifications of respondents that can be useful for cognitively motivated diagnostic…

  1. The Effects of Q-Matrix Design on Classification Accuracy in the Log-Linear Cognitive Diagnosis Model

    ERIC Educational Resources Information Center

    Madison, Matthew J.; Bradshaw, Laine P.

    2015-01-01

    Diagnostic classification models are psychometric models that aim to classify examinees according to their mastery or non-mastery of specified latent characteristics. These models are well-suited for providing diagnostic feedback on educational assessments because of their practical efficiency and increased reliability when compared with other…

  2. Comparison of Activity Type Classification Accuracy from Accelerometers Worn on the Hip, Wrists, and Thigh in Young, Apparently Healthy Adults

    ERIC Educational Resources Information Center

    Montoye, Alexander H. K.; Pivarnik, James M.; Mudd, Lanay M.; Biswas, Subir; Pfeiffer, Karin A.

    2016-01-01

    The purpose of this article is to compare accuracy of activity type prediction models for accelerometers worn on the hip, wrists, and thigh. Forty-four adults performed sedentary, ambulatory, lifestyle, and exercise activities (14 total, 10 categories) for 3-10 minutes each in a 90-minute semi-structured laboratory protocol. Artificial neural…

  3. Classification Accuracy of MMPI-2 Validity Scales in the Detection of Pain-Related Malingering: A Known-Groups Study

    ERIC Educational Resources Information Center

    Bianchini, Kevin J.; Etherton, Joseph L.; Greve, Kevin W.; Heinly, Matthew T.; Meyers, John E.

    2008-01-01

    The purpose of this study was to determine the accuracy of "Minnesota Multiphasic Personality Inventory" 2nd edition (MMPI-2; Butcher, Dahlstrom, Graham, Tellegen, & Kaemmer, 1989) validity indicators in the detection of malingering in clinical patients with chronic pain using a hybrid clinical-known groups/simulator design. The sample consisted…

  4. Accuracy of reported flash point values on material safety data sheets and the impact on product classification.

    PubMed

    Radnoff, Diane

    2013-01-01

    Material Safety Data Sheets (MSDSs) are the foundation of worker right-to-know legislation for chemical hazards. Suppliers can use product test data to determine a product's classification. Alternatively, they may use evaluation and professional judgment based on test results for the product or a product, material, or substance with similar properties. While the criteria for classifying products under the new Globally Harmonized System of Classification and Labeling of Chemicals (GHS) are different, a similar process is followed. Neither the current Workplace Hazardous Materials Information System (WHMIS) nor GHS require suppliers to test their products to classify them. In this project 83 samples of products classified as flammable or combustible, representing a variety of industry sectors and product types, were collected. Flash points were measured and compared to the reported values on the MSDSs. The classifications of the products were then compared using the WHMIS and GHS criteria. The results of the study indicated that there were significant variations between the disclosed and measured flash point values. Overall, more than one-third of the products had flash points lower than that disclosed on the MSDS. In some cases, the measured values were more than 20°C lower than the disclosed values. This could potentially result in an underestimation regarding the flammability of the product so it is important for employers to understand the limitations in the information provided on MSDSs when developing safe work procedures and training programs in the workplace. Nearly one-fifth of the products were misclassified under the WHMIS system as combustible when the measured flash point indicated that they should be classified as flammable when laboratory measurement error was taken into account. While a similar number of products were misclassified using GHS criteria, the tendency appeared to be to "over-classify" (provide a hazard class that was more conservative

  5. Strategies for Achieving High Sequencing Accuracy for Low Diversity Samples and Avoiding Sample Bleeding Using Illumina Platform

    PubMed Central

    Mitra, Abhishek; Skrzypczak, Magdalena; Ginalski, Krzysztof; Rowicka, Maga

    2015-01-01

    analysis can be repeated from saved sequencing images using the Long Template Protocol to increase accuracy. PMID:25860802

  6. Supervised, Multivariate, Whole-Brain Reduction Did Not Help to Achieve High Classification Performance in Schizophrenia Research.

    PubMed

    Janousova, Eva; Montana, Giovanni; Kasparek, Tomas; Schwarz, Daniel

    2016-01-01

    We examined how penalized linear discriminant analysis with resampling, which is a supervised, multivariate, whole-brain reduction technique, can help schizophrenia diagnostics and research. In an experiment with magnetic resonance brain images of 52 first-episode schizophrenia patients and 52 healthy controls, this method allowed us to select brain areas relevant to schizophrenia, such as the left prefrontal cortex, the anterior cingulum, the right anterior insula, the thalamus, and the hippocampus. Nevertheless, the classification performance based on such reduced data was not significantly better than the classification of data reduced by mass univariate selection using a t-test or unsupervised multivariate reduction using principal component analysis. Moreover, we found no important influence of the type of imaging features, namely local deformations or gray matter volumes, and the classification method, specifically linear discriminant analysis or linear support vector machines, on the classification results. However, we ascertained significant effect of a cross-validation setting on classification performance as classification results were overestimated even though the resampling was performed during the selection of brain imaging features. Therefore, it is critically important to perform cross-validation in all steps of the analysis (not only during classification) in case there is no external validation set to avoid optimistically biasing the results of classification studies. PMID:27610072

  7. Supervised, Multivariate, Whole-Brain Reduction Did Not Help to Achieve High Classification Performance in Schizophrenia Research

    PubMed Central

    Janousova, Eva; Montana, Giovanni; Kasparek, Tomas; Schwarz, Daniel

    2016-01-01

    We examined how penalized linear discriminant analysis with resampling, which is a supervised, multivariate, whole-brain reduction technique, can help schizophrenia diagnostics and research. In an experiment with magnetic resonance brain images of 52 first-episode schizophrenia patients and 52 healthy controls, this method allowed us to select brain areas relevant to schizophrenia, such as the left prefrontal cortex, the anterior cingulum, the right anterior insula, the thalamus, and the hippocampus. Nevertheless, the classification performance based on such reduced data was not significantly better than the classification of data reduced by mass univariate selection using a t-test or unsupervised multivariate reduction using principal component analysis. Moreover, we found no important influence of the type of imaging features, namely local deformations or gray matter volumes, and the classification method, specifically linear discriminant analysis or linear support vector machines, on the classification results. However, we ascertained significant effect of a cross-validation setting on classification performance as classification results were overestimated even though the resampling was performed during the selection of brain imaging features. Therefore, it is critically important to perform cross-validation in all steps of the analysis (not only during classification) in case there is no external validation set to avoid optimistically biasing the results of classification studies. PMID:27610072

  8. Supervised, Multivariate, Whole-Brain Reduction Did Not Help to Achieve High Classification Performance in Schizophrenia Research

    PubMed Central

    Janousova, Eva; Montana, Giovanni; Kasparek, Tomas; Schwarz, Daniel

    2016-01-01

    We examined how penalized linear discriminant analysis with resampling, which is a supervised, multivariate, whole-brain reduction technique, can help schizophrenia diagnostics and research. In an experiment with magnetic resonance brain images of 52 first-episode schizophrenia patients and 52 healthy controls, this method allowed us to select brain areas relevant to schizophrenia, such as the left prefrontal cortex, the anterior cingulum, the right anterior insula, the thalamus, and the hippocampus. Nevertheless, the classification performance based on such reduced data was not significantly better than the classification of data reduced by mass univariate selection using a t-test or unsupervised multivariate reduction using principal component analysis. Moreover, we found no important influence of the type of imaging features, namely local deformations or gray matter volumes, and the classification method, specifically linear discriminant analysis or linear support vector machines, on the classification results. However, we ascertained significant effect of a cross-validation setting on classification performance as classification results were overestimated even though the resampling was performed during the selection of brain imaging features. Therefore, it is critically important to perform cross-validation in all steps of the analysis (not only during classification) in case there is no external validation set to avoid optimistically biasing the results of classification studies.

  9. Electrode replacement does not affect classification accuracy in dual-session use of a passive brain-computer interface for assessing cognitive workload

    PubMed Central

    Estepp, Justin R.; Christensen, James C.

    2015-01-01

    The passive brain-computer interface (pBCI) framework has been shown to be a very promising construct for assessing cognitive and affective state in both individuals and teams. There is a growing body of work that focuses on solving the challenges of transitioning pBCI systems from the research laboratory environment to practical, everyday use. An interesting issue is what impact methodological variability may have on the ability to reliably identify (neuro)physiological patterns that are useful for state assessment. This work aimed at quantifying the effects of methodological variability in a pBCI design for detecting changes in cognitive workload. Specific focus was directed toward the effects of replacing electrodes over dual sessions (thus inducing changes in placement, electromechanical properties, and/or impedance between the electrode and skin surface) on the accuracy of several machine learning approaches in a binary classification problem. In investigating these methodological variables, it was determined that the removal and replacement of the electrode suite between sessions does not impact the accuracy of a number of learning approaches when trained on one session and tested on a second. This finding was confirmed by comparing to a control group for which the electrode suite was not replaced between sessions. This result suggests that sensors (both neurological and peripheral) may be removed and replaced over the course of many interactions with a pBCI system without affecting its performance. Future work on multi-session and multi-day pBCI system use should seek to replicate this (lack of) effect between sessions in other tasks, temporal time courses, and data analytic approaches while also focusing on non-stationarity and variable classification performance due to intrinsic factors. PMID:25805963

  10. Achieving plane wave accuracy in linear-scaling density functional theory applied to periodic systems: A case study on crystalline silicon

    NASA Astrophysics Data System (ADS)

    Skylaris, Chris-Kriton; Haynes, Peter D.

    2007-10-01

    Linear-scaling methods for density functional theory promise to revolutionize the scope and scale of first-principles quantum mechanical calculations. Crystalline silicon has been the system of choice for exploratory tests of such methods in the literature, yet attempts at quantitative comparisons under linear-scaling conditions with traditional methods or experimental results have not been forthcoming. A detailed study using the ONETEP code is reported here, demonstrating for the first time that plane wave accuracy can be achieved in linear-scaling calculations on periodic systems.

  11. Improving classification accuracy using multi-date IRS/LISS data and development of thermal stress index for Asiatic lion habitat

    NASA Astrophysics Data System (ADS)

    Gupta, Rajendra Kumar

    The increase in lion and leopard population in the GIR wild life sanctuary and National Park (Gir Protected Area) demands periodic and precision monitoring of habitat at close intervals using space based remote sensing data. Besides characterizing the different forest classes, remote sensing needs to support for the assessment of thermal stress zones and identification of possible corridors for lion dispersion to new home ranges. The study focuses on assessing the thematic forest classification accuracies in percentage terms(CA) attainable using single date post-monsoon (CA=60, kappa = 0.514) as well as leaf shedding (CA=48.4, kappa = 0.372) season data in visible and Near-IR spectral bands of IRS/LISS-III at 23.5 m spatial resolution; and improvement of CA by using joint two date (multi-temporal) data sets (CA=87.2, kappa = 0.843) in the classification. The 188 m spatial resolution IRS/WiFS and 23.5 m spatial resolution LISS-III data were used to study the possible corridors for dispersion of Lions from GIR protected areas (PA). A relative thermal stress index (RTSI) for Gir PA has been developed using NOAA/ AVHRR data sets of post-monsoon, leaf shedded and summer seasons. The paper discusses the role of RTSI as a tool to work out forest management plans using leaf shedded season data to combat the thermal stress in the habitat, by identifying locations for artificial water holes during the ensuing summer season.

  12. Computerized assessment of pedophilic sexual interest through self-report and viewing time: reliability, validity, and classification accuracy of the affinity program.

    PubMed

    Mokros, Andreas; Gebhard, Michael; Heinz, Volker; Marschall, Roland W; Nitschke, Joachim; Glasgow, David V; Gress, Carmen L Z; Laws, D Richard

    2013-06-01

    Affinity is a computerized assessment tool that combines viewing time and self-report measures of sexual interest. The present study was designed to assess the diagnostic properties of Affinity with respect to sexual interest in prepubescent children. Reliability of both self-report and viewing time components was estimated to be high. The group profile of a sample of pedophilic adult male child molesters (n = 42, all of whom admitted their offenses) differed from the group profiles of male community controls (n = 95) and male nonsexual offenders (n = 27), respectively. More specifically, both ratings and viewing times for images showing small children or prejuvenile children were significantly higher within the child molester sample than in either of the other two groups, attesting to the validity of the measures. Overall classification accuracy, however, was mediocre: A multivariate classification routine yielded 50% sensitivity for child molester status at the cost of 13% false positives. The implications for forensic use of Affinity are discussed. PMID:22878565

  13. Achieving Accuracy Requirements for Forest Biomass Mapping: A Data Fusion Method for Estimating Forest Biomass and LiDAR Sampling Error with Spaceborne Data

    NASA Technical Reports Server (NTRS)

    Montesano, P. M.; Cook, B. D.; Sun, G.; Simard, M.; Zhang, Z.; Nelson, R. F.; Ranson, K. J.; Lutchke, S.; Blair, J. B.

    2012-01-01

    The synergistic use of active and passive remote sensing (i.e., data fusion) demonstrates the ability of spaceborne light detection and ranging (LiDAR), synthetic aperture radar (SAR) and multispectral imagery for achieving the accuracy requirements of a global forest biomass mapping mission. This data fusion approach also provides a means to extend 3D information from discrete spaceborne LiDAR measurements of forest structure across scales much larger than that of the LiDAR footprint. For estimating biomass, these measurements mix a number of errors including those associated with LiDAR footprint sampling over regional - global extents. A general framework for mapping above ground live forest biomass (AGB) with a data fusion approach is presented and verified using data from NASA field campaigns near Howland, ME, USA, to assess AGB and LiDAR sampling errors across a regionally representative landscape. We combined SAR and Landsat-derived optical (passive optical) image data to identify forest patches, and used image and simulated spaceborne LiDAR data to compute AGB and estimate LiDAR sampling error for forest patches and 100m, 250m, 500m, and 1km grid cells. Forest patches were delineated with Landsat-derived data and airborne SAR imagery, and simulated spaceborne LiDAR (SSL) data were derived from orbit and cloud cover simulations and airborne data from NASA's Laser Vegetation Imaging Sensor (L VIS). At both the patch and grid scales, we evaluated differences in AGB estimation and sampling error from the combined use of LiDAR with both SAR and passive optical and with either SAR or passive optical alone. This data fusion approach demonstrates that incorporating forest patches into the AGB mapping framework can provide sub-grid forest information for coarser grid-level AGB reporting, and that combining simulated spaceborne LiDAR with SAR and passive optical data are most useful for estimating AGB when measurements from LiDAR are limited because they minimized

  14. Practical Classification Guidelines for Diabetes in patients treated with insulin: a cross-sectional study of the accuracy of diabetes diagnosis

    PubMed Central

    Hope, Suzy V; Wienand-Barnett, Sophie; Shepherd, Maggie; King, Sophie M; Fox, Charles; Khunti, Kamlesh; Oram, Richard A; Knight, Bea A; Hattersley, Andrew T; Jones, Angus G; Shields, Beverley M

    2016-01-01

    Background Differentiating between type 1 and type 2 diabetes is fundamental to ensuring appropriate management of patients, but can be challenging, especially when treating with insulin. The 2010 UK Practical Classification Guidelines for Diabetes were developed to help make the differentiation. Aim To assess diagnostic accuracy of the UK guidelines against ‘gold standard’ definitions of type 1 and type 2 diabetes based on measured C-peptide levels. Design and setting In total, 601 adults with insulin-treated diabetes and diabetes duration ≥5 years were recruited in Devon, Northamptonshire, and Leicestershire. Method Baseline information and home urine sample were collected. Urinary C-peptide creatinine ratio (UCPCR) measures endogenous insulin production. Gold standard type 1 diabetes was defined as continuous insulin treatment within 3 years of diagnosis and absolute insulin deficiency (UCPCR<0.2 nmol/mmol ≥5 years post-diagnosis); all others classed as having type 2 diabetes. Diagnostic performance of the clinical criteria was assessed and other criteria explored using receiver operating characteristic (ROC) curves. Results UK guidelines correctly classified 86% of participants. Most misclassifications occurred in patients classed as having type 1 diabetes who had significant endogenous insulin levels (57 out of 601; 9%); most in those diagnosed ≥35 years and treated with insulin from diagnosis, where 37 out of 66 (56%) were misclassified. Time to insulin and age at diagnosis performed best in predicting long-term endogenous insulin production (ROC AUC = 0.904 and 0.871); BMI was a less strong predictor of diabetes type (AUC = 0.824). Conclusion Current UK guidelines provide a pragmatic clinical approach to classification reflecting long-term endogenous insulin production; caution is needed in older patients commencing insulin from diagnosis, where misclassification rates are increased. PMID:27080317

  15. 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

  16. Relative significance of heat transfer processes to quantify tradeoffs between complexity and accuracy of energy simulations with a building energy use patterns classification

    NASA Astrophysics Data System (ADS)

    Heidarinejad, Mohammad

    This dissertation develops rapid and accurate building energy simulations based on a building classification that identifies and focuses modeling efforts on most significant heat transfer processes. The building classification identifies energy use patterns and their contributing parameters for a portfolio of buildings. The dissertation hypothesis is "Building classification can provide minimal required inputs for rapid and accurate energy simulations for a large number of buildings". The critical literature review indicated there is lack of studies to (1) Consider synoptic point of view rather than the case study approach, (2) Analyze influence of different granularities of energy use, (3) Identify key variables based on the heat transfer processes, and (4) Automate the procedure to quantify model complexity with accuracy. Therefore, three dissertation objectives are designed to test out the dissertation hypothesis: (1) Develop different classes of buildings based on their energy use patterns, (2) Develop different building energy simulation approaches for the identified classes of buildings to quantify tradeoffs between model accuracy and complexity, (3) Demonstrate building simulation approaches for case studies. Penn State's and Harvard's campus buildings as well as high performance LEED NC office buildings are test beds for this study to develop different classes of buildings. The campus buildings include detailed chilled water, electricity, and steam data, enabling to classify buildings into externally-load, internally-load, or mixed-load dominated. The energy use of the internally-load buildings is primarily a function of the internal loads and their schedules. Externally-load dominated buildings tend to have an energy use pattern that is a function of building construction materials and outdoor weather conditions. However, most of the commercial medium-sized office buildings have a mixed-load pattern, meaning the HVAC system and operation schedule dictate

  17. Urban land cover classification using hyperspectral data

    NASA Astrophysics Data System (ADS)

    Hegde, G.; Ahamed, J. Mohammed; Hebbar, R.; Raj, U.

    2014-11-01

    Urban land cover classification using remote sensing data is quite challenging due to spectrally and spatially complex urban features. The present study describes the potential use of hyperspectral data for urban land cover classification and its comparison with multispectral data. EO-1 Hyperion data of October 05, 2012 covering parts of Bengaluru city was analyzed for land cover classification. The hyperspectral data was initially corrected for atmospheric effects using MODTRAN based FLAASH module and Minimum Noise Fraction (MNF) transformation was applied to reduce data dimensionality. The threshold Eigen value of 1.76 in VNIR region and 1.68 in the SWIR region was used for selection of 145 stable bands. Advanced per pixel classifiers viz., Spectral Angle Mapper (SAM) and Support Vector Machine (SVM) were used for general urban land cover classification. Accuracy assessment of the classified data revealed that SVM was quite superior (82.4 per cent) for urban land cover classification as compared to SAM (67.1 per cent). Selecting training samples using end members significantly improved the classification accuracy by 20.1 per cent in SVM. The land cover classification using multispectral LISS-III data using SVM showed lower accuracy mainly due to limitation of spectral resolution. The study indicated the requirement of additional narrow bands for achieving reasonable classification accuracy of urban land cover. Future research is focused on generating hyperspectral library for different urban features.

  18. Hydrometor classification from 2 dimensional videodisdrometer data

    NASA Astrophysics Data System (ADS)

    Grazioli, J.; Tuia, D.; Monhart, S.; Schneebeli, M.; Raupach, T.; Berne, A.

    2014-02-01

    This paper presents a hydrometeor classification technique based on two-dimensional video disdrometer (2DVD) data. The method provides an estimate of the dominant hydrometeor type falling over time intervals of 60 s during precipitation, using as input the statistical behavior of a set of particle descriptors, calculated for each particle image. The employed supervised algorithm is a support vector machine (SVM), trained over precipitation time steps labeled by visual inspection. In this way, 8 dominant hydrometeor classes could be discriminated. The algorithm achieves accurate classification performances, with median overall accuracies (Cohen's K) of 90% (0.88), and with accuracies higher than 84% for each hydrometeor class.

  19. Classification Accuracy of Serum Apo A-I and S100B for the Diagnosis of Mild Traumatic Brain Injury and Prediction of Abnormal Initial Head Computed Tomography Scan

    PubMed Central

    Blyth, Brian J.; He, Hua; Mookerjee, Sohug; Jones, Courtney; Kiechle, Karin; Moynihan, Ryan; Wojcik, Susan M.; Grant, William D.; Secreti, LaLainia M.; Triner, Wayne; Moscati, Ronald; Leinhart, August; Ellis, George L.; Khan, Jawwad

    2013-01-01

    Abstract The objective of the current study was to determine the classification accuracy of serum S100B and apolipoprotein (apoA-I) for mild traumatic brain injury (mTBI) and abnormal initial head computed tomography (CT) scan, and to identify ethnic, racial, age, and sex variation in classification accuracy. We performed a prospective, multi-centered study of 787 patients with mTBI who presented to the emergency department within 6 h of injury and 467 controls who presented to the outpatient laboratory for routine blood work. Serum was analyzed for S100B and apoA-I. The outcomes were disease status (mTBI or control) and initial head CT scan. At cutoff values defined by 90% of controls, the specificity for mTBI using S100B (0.899 [95% confidence interval (CI): 0.78–0.92]) was similar to that using apoA-I (0.902 [0.87–0.93]), and the sensitivity using S100B (0.252 [0.22–0.28]) was similar to that using apoA-I (0.249 [0.22–0.28]). The area under the receiver operating characteristic curve (AUC) for the combination of S100B and apoA-I (0.738, 95% CI: 0.71, 0.77), however, was significantly higher than the AUC for S100B alone (0.709, 95% CI: 0.68, 0.74, p=0.001) and higher than the AUC for apoA-I alone (0.645, 95% CI: 0.61, 0.68, p<0.0001). The AUC for prediction of abnormal initial head CT scan using S100B was 0.694 (95%CI: 0.62, 0.77) and not significant for apoA-I. At a S100B cutoff of <0.060 μg/L, the sensitivity for abnormal head CT was 98%, and 22.9% of CT scans could have been avoided. There was significant age and race-related variation in the accuracy of S100B for the diagnosis of mTBI. The combined use of serum S100B and apoA-I maximizes classification accuracy for mTBI, but only S100B is needed to classify abnormal head CT scan. Because of significant subgroup variation in classification accuracy, age and race need to be considered when using S100B to classify subjects for mTBI. PMID:23758329

  20. Video genre classification using multimodal features

    NASA Astrophysics Data System (ADS)

    Jin, Sung Ho; Bae, Tae Meon; Choo, Jin Ho; Ro, Yong Man

    2003-12-01

    We propose a video genre classification method using multimodal features. The proposed method is applied for the preprocessing of automatic video summarization or the retrieval and classification of broadcasting video contents. Through a statistical analysis of low-level and middle-level audio-visual features in video, the proposed method can achieve good performance in classifying several broadcasting genres such as cartoon, drama, music video, news, and sports. In this paper, we adopt MPEG-7 audio-visual descriptors as multimodal features of video contents and evaluate the performance of the classification by feeding the features into a decision tree-based classifier which is trained by CART. The experimental results show that the proposed method can recognize several broadcasting video genres with a high accuracy and the classification performance with multimodal features is superior to the one with unimodal features in the genre classification.

  1. Improving crop classification through attention to the timing of airborne radar acquisitions

    NASA Technical Reports Server (NTRS)

    Brisco, B.; Ulaby, F. T.; Protz, R.

    1984-01-01

    Radar remote sensors may provide valuable input to crop classification procedures because of (1) their independence of weather conditions and solar illumination, and (2) their ability to respond to differences in crop type. Manual classification of multidate synthetic aperture radar (SAR) imagery resulted in an overall accuracy of 83 percent for corn, forest, grain, and 'other' cover types. Forests and corn fields were identified with accuracies approaching or exceeding 90 percent. Grain fields and 'other' fields were often confused with each other, resulting in classification accuracies of 51 and 66 percent, respectively. The 83 percent correct classification represents a 10 percent improvement when compared to similar SAR data for the same area collected at alternate time periods in 1978. These results demonstrate that improvements in crop classification accuracy can be achieved with SAR data by synchronizing data collection times with crop growth stages in order to maximize differences in the geometric and dielectric properties of the cover types of interest.

  2. Assessment of optimized Markov models in protein fold classification.

    PubMed

    Lampros, Christos; Simos, Thomas; Exarchos, Themis P; Exarchos, Konstantinos P; Papaloukas, Costas; Fotiadis, Dimitrios I

    2014-08-01

    Protein fold classification is a challenging task strongly associated with the determination of proteins' structure. In this work, we tested an optimization strategy on a Markov chain and a recently introduced Hidden Markov Model (HMM) with reduced state-space topology. The proteins with unknown structure were scored against both these models. Then the derived scores were optimized following a local optimization method. The Protein Data Bank (PDB) and the annotation of the Structural Classification of Proteins (SCOP) database were used for the evaluation of the proposed methodology. The results demonstrated that the fold classification accuracy of the optimized HMM was substantially higher compared to that of the Markov chain or the reduced state-space HMM approaches. The proposed methodology achieved an accuracy of 41.4% on fold classification, while Sequence Alignment and Modeling (SAM), which was used for comparison, reached an accuracy of 38%. PMID:25152041

  3. Assessment of the Thematic Accuracy of Land Cover Maps

    NASA Astrophysics Data System (ADS)

    Höhle, J.

    2015-08-01

    Several land cover maps are generated from aerial imagery and assessed by different approaches. The test site is an urban area in Europe for which six classes (`building', `hedge and bush', `grass', `road and parking lot', `tree', `wall and car port') had to be derived. Two classification methods were applied (`Decision Tree' and `Support Vector Machine') using only two attributes (height above ground and normalized difference vegetation index) which both are derived from the images. The assessment of the thematic accuracy applied a stratified design and was based on accuracy measures such as user's and producer's accuracy, and kappa coefficient. In addition, confidence intervals were computed for several accuracy measures. The achieved accuracies and confidence intervals are thoroughly analysed and recommendations are derived from the gained experiences. Reliable reference values are obtained using stereovision, false-colour image pairs, and positioning to the checkpoints with 3D coordinates. The influence of the training areas on the results is studied. Cross validation has been tested with a few reference points in order to derive approximate accuracy measures. The two classification methods perform equally for five classes. Trees are classified with a much better accuracy and a smaller confidence interval by means of the decision tree method. Buildings are classified by both methods with an accuracy of 99% (95% CI: 95%-100%) using independent 3D checkpoints. The average width of the confidence interval of six classes was 14% of the user's accuracy.

  4. Achievements in mental health outcome measurement in Australia: Reflections on progress made by the Australian Mental Health Outcomes and Classification Network (AMHOCN)

    PubMed Central

    2012-01-01

    Background Australia’s National Mental Health Strategy has emphasised the quality, effectiveness and efficiency of services, and has promoted the collection of outcomes and casemix data as a means of monitoring these. All public sector mental health services across Australia now routinely report outcomes and casemix data. Since late-2003, the Australian Mental Health Outcomes and Classification Network (AMHOCN) has received, processed, analysed and reported on outcome data at a national level, and played a training and service development role. This paper documents the history of AMHOCN’s activities and achievements, with a view to providing lessons for others embarking on similar exercises. Method We conducted a desktop review of relevant documents to summarise the history of AMHOCN. Results AMHOCN has operated within a framework that has provided an overarching structure to guide its activities but has been flexible enough to allow it to respond to changing priorities. With no precedents to draw upon, it has undertaken activities in an iterative fashion with an element of ‘trial and error’. It has taken a multi-pronged approach to ensuring that data are of high quality: developing innovative technical solutions; fostering ‘information literacy’; maximising the clinical utility of data at a local level; and producing reports that are meaningful to a range of audiences. Conclusion AMHOCN’s efforts have contributed to routine outcome measurement gaining a firm foothold in Australia’s public sector mental health services. PMID:22640939

  5. Compensatory neurofuzzy model for discrete data classification in biomedical

    NASA Astrophysics Data System (ADS)

    Ceylan, Rahime

    2015-03-01

    Biomedical data is separated to two main sections: signals and discrete data. So, studies in this area are about biomedical signal classification or biomedical discrete data classification. There are artificial intelligence models which are relevant to classification of ECG, EMG or EEG signals. In same way, in literature, many models exist for classification of discrete data taken as value of samples which can be results of blood analysis or biopsy in medical process. Each algorithm could not achieve high accuracy rate on classification of signal and discrete data. In this study, compensatory neurofuzzy network model is presented for classification of discrete data in biomedical pattern recognition area. The compensatory neurofuzzy network has a hybrid and binary classifier. In this system, the parameters of fuzzy systems are updated by backpropagation algorithm. The realized classifier model is conducted to two benchmark datasets (Wisconsin Breast Cancer dataset and Pima Indian Diabetes dataset). Experimental studies show that compensatory neurofuzzy network model achieved 96.11% accuracy rate in classification of breast cancer dataset and 69.08% accuracy rate was obtained in experiments made on diabetes dataset with only 10 iterations.

  6. A localized orbital analysis of the thermochemical errors in hybrid density functional theory: achieving chemical accuracy via a simple empirical correction scheme.

    PubMed

    Friesner, Richard A; Knoll, Eric H; Cao, Yixiang

    2006-09-28

    This paper describes an empirical localized orbital correction model which improves the accuracy of density functional theory (DFT) methods for the prediction of thermochemical properties for molecules of first and second row elements. The B3LYP localized orbital correction version of the model improves B3LYP DFT atomization energy calculations on the G3 data set of 222 molecules from a mean absolute deviation (MAD) from experiment of 4.8 to 0.8 kcal/mol. The almost complete elimination of large outliers and the substantial reduction in MAD yield overall results comparable to the G3 wave-function-based method; furthermore, the new model has zero additional computational cost beyond standard DFT calculations. The following four classes of correction parameters are applied to a molecule based on standard valence bond assignments: corrections to atoms, corrections to individual bonds, corrections for neighboring bonds of a given bond, and radical environmental corrections. Although the model is heuristic and is based on a 22 parameter multiple linear regression to experimental errors, each of the parameters is justified on physical grounds, and each provides insight into the fundamental limitations of DFT, most importantly the failure of current DFT methods to accurately account for nondynamical electron correlation.

  7. The coarse pointing assembly for SILEX program or how to achieve outstanding pointing accuracy with simple hardware associated with consistent control laws

    NASA Astrophysics Data System (ADS)

    Buvat, Daniel; Muller, Gerard; Peyrot, Patrick

    1991-06-01

    Attention is given to the coarse pointing assembly (CPA) for the SILEX program, designed on the basis of 10 years of MATRA experience in very accurate drive mechanisms successfully conducted by the SPOT 1 and 2 flights as well as EURECA IOC. The basic key design feature of the mechanism is a 1200-step stepper motor driven in microstepping with harmonic defects compensation. This allows very low torque noise associated with a high accuracy (0.01 deg). The direct drive principle avoids backlash and permits a linear control of the output shaft of each drive. The only parts susceptible to possible wear are the ball bearings, which have a design margin of greater than 1000 for 10 yr of service life. In order to meet the dynamic performances required by SILEX, a closed loop active damping system is added to each drive unit. Two accelerometers used in a differential way sense the hinge microvibrations and an active damping loop reduces their Q factor down to a few dB. All CPA electrical parts (including motor, optical encoder, and accelerometer) are redundant to avoid single point of failure.

  8. Effect of various binning methods and ROI sizes on the accuracy of the automatic classification system for differentiation between diffuse infiltrative lung diseases on the basis of texture features at HRCT

    NASA Astrophysics Data System (ADS)

    Kim, Namkug; Seo, Joon Beom; Sung, Yu Sub; Park, Bum-Woo; Lee, Youngjoo; Park, Seong Hoon; Lee, Young Kyung; Kang, Suk-Ho

    2008-03-01

    To find optimal binning, variable binning size linear binning (LB) and non-linear binning (NLB) methods were tested. In case of small binning size (Q <= 10), NLB shows significant better accuracy than the LB. K-means NLB (Q = 26) is statistically significant better than every LB. To find optimal binning method and ROI size of the automatic classification system for differentiation between diffuse infiltrative lung diseases on the basis of textural analysis at HRCT Six-hundred circular regions of interest (ROI) with 10, 20, and 30 pixel diameter, comprising of each 100 ROIs representing six regional disease patterns (normal, NL; ground-glass opacity, GGO; reticular opacity, RO; honeycombing, HC; emphysema, EMPH; and consolidation, CONS) were marked by an experienced radiologist from HRCT images. Histogram (mean) and co-occurrence matrix (mean and SD of angular second moment, contrast, correlation, entropy, and inverse difference momentum) features were employed to test binning and ROI effects. To find optimal binning, variable binning size LB (bin size Q: 4~30, 32, 64, 128, 144, 196, 256, 384) and NLB (Q: 4~30) methods (K-means, and Fuzzy C-means clustering) were tested. For automated classification, a SVM classifier was implemented. To assess cross-validation of the system, a five-folding method was used. Each test was repeatedly performed twenty times. Overall accuracies with every combination of variable ROIs, and binning sizes were statistically compared. In case of small binning size (Q <= 10), NLB shows significant better accuracy than the LB. K-means NLB (Q = 26) is statistically significant better than every LB. In case of 30x30 ROI size and most of binning size, the K-means method showed better than other NLB and LB methods. When optimal binning and other parameters were set, overall sensitivity of the classifier was 92.85%. The sensitivity and specificity of the system for each class were as follows: NL, 95%, 97.9%; GGO, 80%, 98.9%; RO 85%, 96.9%; HC, 94

  9. Injury narrative text classification using factorization model

    PubMed Central

    2015-01-01

    Narrative text is a useful way of identifying injury circumstances from the routine emergency department data collections. Automatically classifying narratives based on machine learning techniques is a promising technique, which can consequently reduce the tedious manual classification process. Existing works focus on using Naive Bayes which does not always offer the best performance. This paper proposes the Matrix Factorization approaches along with a learning enhancement process for this task. The results are compared with the performance of various other classification approaches. The impact on the classification results from the parameters setting during the classification of a medical text dataset is discussed. With the selection of right dimension k, Non Negative Matrix Factorization-model method achieves 10 CV accuracy of 0.93. PMID:26043671

  10. Classification of ASKAP Vast Radio Light Curves

    NASA Technical Reports Server (NTRS)

    Rebbapragada, Umaa; Lo, Kitty; Wagstaff, Kiri L.; Reed, Colorado; Murphy, Tara; Thompson, David R.

    2012-01-01

    The VAST survey is a wide-field survey that observes with unprecedented instrument sensitivity (0.5 mJy or lower) and repeat cadence (a goal of 5 seconds) that will enable novel scientific discoveries related to known and unknown classes of radio transients and variables. Given the unprecedented observing characteristics of VAST, it is important to estimate source classification performance, and determine best practices prior to the launch of ASKAP's BETA in 2012. The goal of this study is to identify light curve characterization and classification algorithms that are best suited for archival VAST light curve classification. We perform our experiments on light curve simulations of eight source types and achieve best case performance of approximately 90% accuracy. We note that classification performance is most influenced by light curve characterization rather than classifier algorithm.

  11. Hydrometeor classification from two-dimensional video disdrometer data

    NASA Astrophysics Data System (ADS)

    Grazioli, J.; Tuia, D.; Monhart, S.; Schneebeli, M.; Raupach, T.; Berne, A.

    2014-09-01

    The first hydrometeor classification technique based on two-dimensional video disdrometer (2DVD) data is presented. The method provides an estimate of the dominant hydrometeor type falling over time intervals of 60 s during precipitation, using the statistical behavior of a set of particle descriptors as input, calculated for each particle image. The employed supervised algorithm is a support vector machine (SVM), trained over 60 s precipitation time steps labeled by visual inspection. In this way, eight dominant hydrometeor classes can be discriminated. The algorithm achieved high classification performances, with median overall accuracies (Cohen's K) of 90% (0.88), and with accuracies higher than 84% for each hydrometeor class.

  12. Semantic labeling of digital photos by classification

    NASA Astrophysics Data System (ADS)

    Ciocca, Gianluigi; Cusano, Claudio; Schettini, Raimondo; Brambilla, Carla

    2003-01-01

    The paper addresses the problem of annotating photographs with broad semantic labels. To cope with the great variety of photos available on the WEB we have designed a hierarchical classification strategy which first classifies images as pornographic or not-pornographic. Not-pornographic images are then classified as indoor, outdoor, or close-up. On a database of over 9000 images, mostly downloaded from the web, our method achieves an average accuracy of close to 90%.

  13. Classification of finger movements for the dexterous hand prosthesis control with surface electromyography.

    PubMed

    Al-Timemy, Ali H; Bugmann, Guido; Escudero, Javier; Outram, Nicholas

    2013-05-01

    A method for the classification of finger movements for dexterous control of prosthetic hands is proposed. Previous research was mainly devoted to identify hand movements as these actions generate strong electromyography (EMG) signals recorded from the forearm. In contrast, in this paper, we assess the use of multichannel surface electromyography (sEMG) to classify individual and combined finger movements for dexterous prosthetic control. sEMG channels were recorded from ten intact-limbed and six below-elbow amputee persons. Offline processing was used to evaluate the classification performance. The results show that high classification accuracies can be achieved with a processing chain consisting of time domain-autoregression feature extraction, orthogonal fuzzy neighborhood discriminant analysis for feature reduction, and linear discriminant analysis for classification. We show that finger and thumb movements can be decoded accurately with high accuracy with latencies as short as 200 ms. Thumb abduction was decoded successfully with high accuracy for six amputee persons for the first time. We also found that subsets of six EMG channels provide accuracy values similar to those computed with the full set of EMG channels (98% accuracy over ten intact-limbed subjects for the classification of 15 classes of different finger movements and 90% accuracy over six amputee persons for the classification of 12 classes of individual finger movements). These accuracy values are higher than previous studies, whereas we typically employed half the number of EMG channels per identified movement.

  14. 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.

  15. Classification of surface EMG signals using optimal wavelet packet method based on Davies-Bouldin criterion.

    PubMed

    Wang, Gang; Wang, Zhizhong; Chen, Weiting; Zhuang, Jun

    2006-10-01

    In this paper we present an optimal wavelet packet (OWP) method based on Davies-Bouldin criterion for the classification of surface electromyographic signals. To reduce the feature dimensionality of the outputs of the OWP decomposition, the principle components analysis was employed. Then we chose a neural network classifier to discriminate four types of prosthesis movements. The proposed method achieved a mean classification accuracy of 93.75%, which outperformed the method using the energy of wavelet packet coefficients (with mean classification accuracy 86.25%) and the fuzzy wavelet packet method (87.5%).

  16. Agricultural Land Use classification from Envisat MERIS

    NASA Astrophysics Data System (ADS)

    Brodsky, L.; Kodesova, R.

    2009-04-01

    This study focuses on evaluation of a crop classification from middle-resolution images (Envisat MERIS) at national level. The main goal of such Land Use product is to provid spatial data for optimisation of monitoring of surface and groundwater pollution in the Czech Republic caused by pesticides use in agriculture. As there is a lack of spatial data on the pesticide use and their distribution, the localisation can be done according to the crop cover on arable land derived from the remote sensing images. Often high resolution data are used for agricultural Land Use classification but only at regional or local level. Envisat MERIS data, due to the wide satellite swath, can be used also at national level. The high temporal and also spectral resolution of MERIS data has indisputable advantage for crop classification. Methodology of a pixel-based MERIS classification applying an artificial neural-network (ANN) technique was proposed and performed at a national level, the Czech Republic. Five crop groups were finally selected - winter crops, spring crops, summer crops and other crops to be classified. Classification models included a linear, radial basis function (RBF) and a multi-layer percepton (MLP) ANN with 50 networks tested in training. The training data set consisted of about 200 samples per class, on which bootstrap resampling was applied. Selection of a subset of independent variables (Meris spectral channels) was used in the procedure. The best selected ANN model (MLP: 3 in, 13 hidden, 3 out) resulted in very good performance (correct classification rate 0.974, error 0.103) applying three crop types data set. In the next step data set with five crop types was evaluated. The ANN model (MLP: 5 in, 12 hidden, 5 out) performance was also very good (correct classification rate 0.930, error 0.370). The study showed, that while accuracy of about 80 % was achieved at pixel level when classifying only three crops, accuracy of about 70 % was achieved for five crop

  17. Real-time, resource-constrained object classification on a micro-air vehicle

    NASA Astrophysics Data System (ADS)

    Buck, Louis; Ray, Laura

    2013-12-01

    A real-time embedded object classification algorithm is developed through the novel combination of binary feature descriptors, a bag-of-visual-words object model and the cortico-striatal loop (CSL) learning algorithm. The BRIEF, ORB and FREAK binary descriptors are tested and compared to SIFT descriptors with regard to their respective classification accuracies, execution times, and memory requirements when used with CSL on a 12.6 g ARM Cortex embedded processor running at 800 MHz. Additionally, the effect of x2 feature mapping and opponent-color representations used with these descriptors is examined. These tests are performed on four data sets of varying sizes and difficulty, and the BRIEF descriptor is found to yield the best combination of speed and classification accuracy. Its use with CSL achieves accuracies between 67% and 95% of those achieved with SIFT descriptors and allows for the embedded classification of a 128x192 pixel image in 0.15 seconds, 60 times faster than classification with SIFT. X2 mapping is found to provide substantial improvements in classification accuracy for all of the descriptors at little cost, while opponent-color descriptors are offer accuracy improvements only on colorful datasets.

  18. Temporal context in floristic classification

    NASA Astrophysics Data System (ADS)

    Fitzgerald, R. W.; Lees, B. G.

    1996-11-01

    Multi-temporal remote sensing data present a number of significant problems for the statistical and spatial competence of a classifier. Ideally, a classifier of multi-temporal data should be temporally invariant. It must have the capacity to account for the variations in season, growth cycle, radiometric, and atmospheric conditions at any point in time when classifying the land cover. This paper tests two methods of creating a temporally invariant classifier based on the pattern recognition capabilities of a neural network. A suite of twelve multi-temporal datasets spread over 5 yr along with a comprehensive mix of environmental variables are fused into floristic classification images by the neural network. Uncertainties in the classifications are addressed explicitly with a confidence mask generated from the fuzzy membership value's output by the neural network. These confidence masks are used to produce constrained classification images. The overall accuracy percentage achieved from a study site containing highly disturbed undulating terrain averages 60%. The first method of training, sequential learning of temporal context, is tested by an examination of the step-by-step evolution of the sequential training process. This reveals that the sequential classifier may not have learned about time, because time was constant during each network training session. It also suggests that there are optimal times during the annual cycle to train the classifier for particular floristic classes. The second method of training the classifier is randomised exposure to the entire temporal training suite. Time was now a fluctuating input variable during the network training process. This method produced the best spatially accurate results. The performance of this classifier as a temporally invariant classifier is tested amongst four multi-temporal datasets with encouraging results. The classifier consistently achieved an overall accuracy percentage of 60%. The pairwise predicted

  19. Automated Classification of Clinical Incident Types.

    PubMed

    Gupta, Jaiprakash; Koprinska, Irena; Patrick, Jon

    2015-01-01

    We consider the task of automatic classification of clinical incident reports using machine learning methods. Our data consists of 5448 clinical incident reports collected from the Incident Information Management System used by 7 hospitals in the state of New South Wales in Australia. We evaluate the performance of four classification algorithms: decision tree, naïve Bayes, multinomial naïve Bayes and support vector machine. We initially consider 13 classes (incident types) that were then reduced to 12, and show that it is possible to build accurate classifiers. The most accurate classifier was the multinomial naïve Bayes achieving accuracy of 80.44% and AUC of 0.91. We also investigate the effect of class labelling by an ordinary clinician and an expert, and show that when the data is labelled by an expert the classification performance of all classifiers improves. We found that again the best classifier was multinomial naïve Bayes achieving accuracy of 81.32% and AUC of 0.97. Our results show that some classes in the Incident Information Management System such as Primary Care are not distinct and their removal can improve performance; some other classes such as Aggression Victim are easier to classify than others such as Behavior and Human Performance. In summary, we show that the classification performance can be improved by expert class labelling of the training data, removing classes that are not well defined and selecting appropriate machine learning classifiers. PMID:26210423

  20. Ground Truth Sampling and LANDSAT Accuracy Assessment

    NASA Technical Reports Server (NTRS)

    Robinson, J. W.; Gunther, F. J.; Campbell, W. J.

    1982-01-01

    It is noted that the key factor in any accuracy assessment of remote sensing data is the method used for determining the ground truth, independent of the remote sensing data itself. The sampling and accuracy procedures developed for nuclear power plant siting study are described. The purpose of the sampling procedure was to provide data for developing supervised classifications for two study sites and for assessing the accuracy of that and the other procedures used. The purpose of the accuracy assessment was to allow the comparison of the cost and accuracy of various classification procedures as applied to various data types.

  1. D Land Cover Classification Based on Multispectral LIDAR Point Clouds

    NASA Astrophysics Data System (ADS)

    Zou, Xiaoliang; Zhao, Guihua; Li, Jonathan; Yang, Yuanxi; Fang, Yong

    2016-06-01

    Multispectral Lidar System can emit simultaneous laser pulses at the different wavelengths. The reflected multispectral energy is captured through a receiver of the sensor, and the return signal together with the position and orientation information of sensor is recorded. These recorded data are solved with GNSS/IMU data for further post-processing, forming high density multispectral 3D point clouds. As the first commercial multispectral airborne Lidar sensor, Optech Titan system is capable of collecting point clouds data from all three channels at 532nm visible (Green), at 1064 nm near infrared (NIR) and at 1550nm intermediate infrared (IR). It has become a new source of data for 3D land cover classification. The paper presents an Object Based Image Analysis (OBIA) approach to only use multispectral Lidar point clouds datasets for 3D land cover classification. The approach consists of three steps. Firstly, multispectral intensity images are segmented into image objects on the basis of multi-resolution segmentation integrating different scale parameters. Secondly, intensity objects are classified into nine categories by using the customized features of classification indexes and a combination the multispectral reflectance with the vertical distribution of object features. Finally, accuracy assessment is conducted via comparing random reference samples points from google imagery tiles with the classification results. The classification results show higher overall accuracy for most of the land cover types. Over 90% of overall accuracy is achieved via using multispectral Lidar point clouds for 3D land cover classification.

  2. Wavelet packet entropy for heart murmurs classification.

    PubMed

    Safara, Fatemeh; Doraisamy, Shyamala; Azman, Azreen; Jantan, Azrul; Ranga, Sri

    2012-01-01

    Heart murmurs are the first signs of cardiac valve disorders. Several studies have been conducted in recent years to automatically differentiate normal heart sounds, from heart sounds with murmurs using various types of audio features. Entropy was successfully used as a feature to distinguish different heart sounds. In this paper, new entropy was introduced to analyze heart sounds and the feasibility of using this entropy in classification of five types of heart sounds and murmurs was shown. The entropy was previously introduced to analyze mammograms. Four common murmurs were considered including aortic regurgitation, mitral regurgitation, aortic stenosis, and mitral stenosis. Wavelet packet transform was employed for heart sound analysis, and the entropy was calculated for deriving feature vectors. Five types of classification were performed to evaluate the discriminatory power of the generated features. The best results were achieved by BayesNet with 96.94% accuracy. The promising results substantiate the effectiveness of the proposed wavelet packet entropy for heart sounds classification.

  3. Urban Tree Classification Using Full-Waveform Airborne Laser Scanning

    NASA Astrophysics Data System (ADS)

    Koma, Zs.; Koenig, K.; Höfle, B.

    2016-06-01

    Vegetation mapping in urban environments plays an important role in biological research and urban management. Airborne laser scanning provides detailed 3D geodata, which allows to classify single trees into different taxa. Until now, research dealing with tree classification focused on forest environments. This study investigates the object-based classification of urban trees at taxonomic family level, using full-waveform airborne laser scanning data captured in the city centre of Vienna (Austria). The data set is characterised by a variety of taxa, including deciduous trees (beeches, mallows, plane trees and soapberries) and the coniferous pine species. A workflow for tree object classification is presented using geometric and radiometric features. The derived features are related to point density, crown shape and radiometric characteristics. For the derivation of crown features, a prior detection of the crown base is performed. The effects of interfering objects (e.g. fences and cars which are typical in urban areas) on the feature characteristics and the subsequent classification accuracy are investigated. The applicability of the features is evaluated by Random Forest classification and exploratory analysis. The most reliable classification is achieved by using the combination of geometric and radiometric features, resulting in 87.5% overall accuracy. By using radiometric features only, a reliable classification with accuracy of 86.3% can be achieved. The influence of interfering objects on feature characteristics is identified, in particular for the radiometric features. The results indicate the potential of using radiometric features in urban tree classification and show its limitations due to anthropogenic influences at the same time.

  4. Classification of Physical Activity

    PubMed Central

    Turksoy, Kamuran; Paulino, Thiago Marques Luz; Zaharieva, Dessi P.; Yavelberg, Loren; Jamnik, Veronica; Riddell, Michael C.; Cinar, Ali

    2015-01-01

    Physical activity has a wide range of effects on glucose concentrations in type 1 diabetes (T1D) depending on the type (ie, aerobic, anaerobic, mixed) and duration of activity performed. This variability in glucose responses to physical activity makes the development of artificial pancreas (AP) systems challenging. Automatic detection of exercise type and intensity, and its classification as aerobic or anaerobic would provide valuable information to AP control algorithms. This can be achieved by using a multivariable AP approach where biometric variables are measured and reported to the AP at high frequency. We developed a classification system that identifies, in real time, the exercise intensity and its reliance on aerobic or anaerobic metabolism and tested this approach using clinical data collected from 5 persons with T1D and 3 individuals without T1D in a controlled laboratory setting using a variety of common types of physical activity. The classifier had an average sensitivity of 98.7% for physiological data collected over a range of exercise modalities and intensities in these subjects. The classifier will be added as a new module to the integrated multivariable adaptive AP system to enable the detection of aerobic and anaerobic exercise for enhancing the accuracy of insulin infusion strategies during and after exercise. PMID:26443291

  5. Discoveries, Achievements, and Personalities of the Women Who Evolved the Harvard Classification of Stellar Spectra: Williamina Fleming, Antonia Maury, and Annie Jump Cannon.

    NASA Astrophysics Data System (ADS)

    Welther, Barbara L.

    2010-01-01

    In 1915, the year in which Cannon (1863-1941) completed her work of classifying stars for The Henry Draper Catalogue, she published a popular article entitled, "Pioneering in the Classification of Stellar Spectra.” In it she gave a historical overview of the field in nineteenth-century Europe. She also detailed the context for the structured and routine work she and her colleagues had been engaged in for several years in America. The motivators that kept Cannon and the other women working diligently were the exciting prospect of making new discoveries, the reward of publicity, and their own personal pride. Usually, the discoveries consisted of finding a peculiar type of spectrum and identifying the star as a nova or variable. Such a discovery often resulted in a newspaper headline about the star and a story about the discoverer. This paper will outline the contributions each woman made to the classification system, her style of working, the papers she wrote and published, and the rewards she reaped for her dedication to the field.

  6. Color image classification systems for poultry viscera inspection

    NASA Astrophysics Data System (ADS)

    Chao, Kevin; Chen, Yud-Ren; Early, Howard; Park, Bosoon

    1999-01-01

    A neuro-fuzzy based image classification system that utilizes color-imaging features of poultry viscera in the spectral and spatial domains was developed in this study. Poultry viscera of liver and heart were separated into four classes: normal, airsacculitis, cadaver, and septicemia. Color images for the classified poultry viscera were collected in the poultry process plant. These images in RGB color space were segmented and statistical analysis was performed for feature selection. The neuro-fuzzy system utilizes hybrid paradigms of fuzzy interference system and neural networks to enhance the robustness of the classification processes. The results showed that the accuracy for separation of normal from abnormal livers were 87.5 to 92.5% when two classes of validation data were used. For two-class classification of chicken hearts, the accuracies were 92.5 to 97.5%. When neuro-fuzzy models were employed to separate chicken livers into three classes (normal, airsacculitis, and cadaver), the accuracy was 88.3% for the training data and 83.3% for the validation data. Combining features of chicken liver and heart, a generalized neuro-fuzzy model was designed to classify poultry viscera into four classes (normal, airsacculitis, cadaver, and septicemia). The classification accuracy of 86.3% was achieved for the training data and 82.5% accuracy for the validation.

  7. Evaluation of space SAR as a land-cover classification

    NASA Technical Reports Server (NTRS)

    Brisco, B.; Ulaby, F. T.; Williams, T. H. L.

    1985-01-01

    The multidimensional approach to the mapping of land cover, crops, and forests is reported. Dimensionality is achieved by using data from sensors such as LANDSAT to augment Seasat and Shuttle Image Radar (SIR) data, using different image features such as tone and texture, and acquiring multidate data. Seasat, Shuttle Imaging Radar (SIR-A), and LANDSAT data are used both individually and in combination to map land cover in Oklahoma. The results indicates that radar is the best single sensor (72% accuracy) and produces the best sensor combination (97.5% accuracy) for discriminating among five land cover categories. Multidate Seasat data and a single data of LANDSAT coverage are then used in a crop classification study of western Kansas. The highest accuracy for a single channel is achieved using a Seasat scene, which produces a classification accuracy of 67%. Classification accuracy increases to approximately 75% when either a multidate Seasat combination or LANDSAT data in a multisensor combination is used. The tonal and textural elements of SIR-A data are then used both alone and in combination to classify forests into five categories.

  8. EEG Classification of Different Imaginary Movements within the Same Limb

    PubMed Central

    Yong, Xinyi; Menon, Carlo

    2015-01-01

    The task of discriminating the motor imagery of different movements within the same limb using electroencephalography (EEG) signals is challenging because these imaginary movements have close spatial representations on the motor cortex area. There is, however, a pressing need to succeed in this task. The reason is that the ability to classify different same-limb imaginary movements could increase the number of control dimensions of a brain-computer interface (BCI). In this paper, we propose a 3-class BCI system that discriminates EEG signals corresponding to rest, imaginary grasp movements, and imaginary elbow movements. Besides, the differences between simple motor imagery and goal-oriented motor imagery in terms of their topographical distributions and classification accuracies are also being investigated. To the best of our knowledge, both problems have not been explored in the literature. Based on the EEG data recorded from 12 able-bodied individuals, we have demonstrated that same-limb motor imagery classification is possible. For the binary classification of imaginary grasp and elbow (goal-oriented) movements, the average accuracy achieved is 66.9%. For the 3-class problem of discriminating rest against imaginary grasp and elbow movements, the average classification accuracy achieved is 60.7%, which is greater than the random classification accuracy of 33.3%. Our results also show that goal-oriented imaginary elbow movements lead to a better classification performance compared to simple imaginary elbow movements. This proposed BCI system could potentially be used in controlling a robotic rehabilitation system, which can assist stroke patients in performing task-specific exercises. PMID:25830611

  9. An improved methodology for land-cover classification using artificial neural networks and a decision tree classifier

    NASA Astrophysics Data System (ADS)

    Arellano-Neri, Olimpia

    Mapping is essential for the analysis of the land and land-cover dynamics, which influence many environmental processes and properties. When creating land-cover maps it is important to minimize error, since error will propagate into later analyses based upon these land cover maps. The reliability of land cover maps derived from remotely sensed data depends upon an accurate classification. For decades, traditional statistical methods have been applied in land-cover classification with varying degrees of accuracy. One of the most significant developments in the field of land-cover classification using remotely sensed data has been the introduction of Artificial Neural Networks (ANN) procedures. In this research, Artificial Neural Networks were applied to remotely sensed data of the southwestern Ohio region for land-cover classification. Three variants on traditional ANN-based classifiers were explored here: (1) the use of a customized architecture of the neural network in terms of the input layer for each land-cover class, (2) the use of texture analysis to combine spectral information and spatial information which is essential for urban classes, and (3) the use of decision tree (DT) classification to refine the ANN classification and ultimately to achieve a more reliable land-cover thematic map. The objective of this research was to prove that a classification based on Artificial Neural Networks (ANN) and decision tree (DT) would outperform by far the National Land Cover Data (NLCD). The NLCD is a land-cover classification produced by a cooperative effort between the United States Geological Survey (USGS) and the United States Environmental Protection Agency (USEPA). In order to achieve this objective, an accuracy assessment was conducted for both NLCD classification and ANN/DT classification. Error matrices resulting from the accuracy assessments provided overall accuracy, accuracy of each class, omission errors, and commission errors for each classification. The

  10. Remote Sensing Data Binary Classification Using Boosting with Simple Classifiers

    NASA Astrophysics Data System (ADS)

    Nowakowski, Artur

    2015-10-01

    Boosting is a classification method which has been proven useful in non-satellite image processing while it is still new to satellite remote sensing. It is a meta-algorithm, which builds a strong classifier from many weak ones in iterative way. We adapt the AdaBoost.M1 boosting algorithm in a new land cover classification scenario based on utilization of very simple threshold classifiers employing spectral and contextual information. Thresholds for the classifiers are automatically calculated adaptively to data statistics. The proposed method is employed for the exemplary problem of artificial area identification. Classification of IKONOS multispectral data results in short computational time and overall accuracy of 94.4% comparing to 94.0% obtained by using AdaBoost.M1 with trees and 93.8% achieved using Random Forest. The influence of a manipulation of the final threshold of the strong classifier on classification results is reported.

  11. Hyperspectral Data Classification Using Factor Graphs

    NASA Astrophysics Data System (ADS)

    Makarau, A.; Müller, R.; Palubinskas, G.; Reinartz, P.

    2012-07-01

    Accurate classification of hyperspectral data is still a competitive task and new classification methods are developed to achieve desired tasks of hyperspectral data use. The objective of this paper is to develop a new method for hyperspectral data classification ensuring the classification model properties like transferability, generalization, probabilistic interpretation, etc. While factor graphs (undirected graphical models) are unfortunately not widely employed in remote sensing tasks, these models possess important properties such as representation of complex systems to model estimation/decision making tasks. In this paper we present a new method for hyperspectral data classification using factor graphs. Factor graph (a bipartite graph consisting of variables and factor vertices) allows factorization of a more complex function leading to definition of variables (employed to store input data), latent variables (allow to bridge abstract class to data), and factors (defining prior probabilities for spectral features and abstract classes; input data mapping to spectral features mixture and further bridging of the mixture to an abstract class). Latent variables play an important role by defining two-level mapping of the input spectral features to a class. Configuration (learning) on training data of the model allows calculating a parameter set for the model to bridge the input data to a class. The classification algorithm is as follows. Spectral bands are separately pre-processed (unsupervised clustering is used) to be defined on a finite domain (alphabet) leading to a representation of the data on multinomial distribution. The represented hyperspectral data is used as input evidence (evidence vector is selected pixelwise) in a configured factor graph and an inference is run resulting in the posterior probability. Variational inference (Mean field) allows to obtain plausible results with a low calculation time. Calculating the posterior probability for each class

  12. Skimming Digits: Neuromorphic Classification of Spike-Encoded Images.

    PubMed

    Cohen, Gregory K; Orchard, Garrick; Leng, Sio-Hoi; Tapson, Jonathan; Benosman, Ryad B; van Schaik, André

    2016-01-01

    The growing demands placed upon the field of computer vision have renewed the focus on alternative visual scene representations and processing paradigms. Silicon retinea provide an alternative means of imaging the visual environment, and produce frame-free spatio-temporal data. This paper presents an investigation into event-based digit classification using N-MNIST, a neuromorphic dataset created with a silicon retina, and the Synaptic Kernel Inverse Method (SKIM), a learning method based on principles of dendritic computation. As this work represents the first large-scale and multi-class classification task performed using the SKIM network, it explores different training patterns and output determination methods necessary to extend the original SKIM method to support multi-class problems. Making use of SKIM networks applied to real-world datasets, implementing the largest hidden layer sizes and simultaneously training the largest number of output neurons, the classification system achieved a best-case accuracy of 92.87% for a network containing 10,000 hidden layer neurons. These results represent the highest accuracies achieved against the dataset to date and serve to validate the application of the SKIM method to event-based visual classification tasks. Additionally, the study found that using a square pulse as the supervisory training signal produced the highest accuracy for most output determination methods, but the results also demonstrate that an exponential pattern is better suited to hardware implementations as it makes use of the simplest output determination method based on the maximum value. PMID:27199646

  13. Skimming Digits: Neuromorphic Classification of Spike-Encoded Images

    PubMed Central

    Cohen, Gregory K.; Orchard, Garrick; Leng, Sio-Hoi; Tapson, Jonathan; Benosman, Ryad B.; van Schaik, André

    2016-01-01

    The growing demands placed upon the field of computer vision have renewed the focus on alternative visual scene representations and processing paradigms. Silicon retinea provide an alternative means of imaging the visual environment, and produce frame-free spatio-temporal data. This paper presents an investigation into event-based digit classification using N-MNIST, a neuromorphic dataset created with a silicon retina, and the Synaptic Kernel Inverse Method (SKIM), a learning method based on principles of dendritic computation. As this work represents the first large-scale and multi-class classification task performed using the SKIM network, it explores different training patterns and output determination methods necessary to extend the original SKIM method to support multi-class problems. Making use of SKIM networks applied to real-world datasets, implementing the largest hidden layer sizes and simultaneously training the largest number of output neurons, the classification system achieved a best-case accuracy of 92.87% for a network containing 10,000 hidden layer neurons. These results represent the highest accuracies achieved against the dataset to date and serve to validate the application of the SKIM method to event-based visual classification tasks. Additionally, the study found that using a square pulse as the supervisory training signal produced the highest accuracy for most output determination methods, but the results also demonstrate that an exponential pattern is better suited to hardware implementations as it makes use of the simplest output determination method based on the maximum value. PMID:27199646

  14. Dynamic time warping and sparse representation classification for birdsong phrase classification using limited training data.

    PubMed

    Tan, Lee N; Alwan, Abeer; Kossan, George; Cody, Martin L; Taylor, Charles E

    2015-03-01

    Annotation of phrases in birdsongs can be helpful to behavioral and population studies. To reduce the need for manual annotation, an automated birdsong phrase classification algorithm for limited data is developed. Limited data occur because of limited recordings or the existence of rare phrases. In this paper, classification of up to 81 phrase classes of Cassin's Vireo is performed using one to five training samples per class. The algorithm involves dynamic time warping (DTW) and two passes of sparse representation (SR) classification. DTW improves the similarity between training and test phrases from the same class in the presence of individual bird differences and phrase segmentation inconsistencies. The SR classifier works by finding a sparse linear combination of training feature vectors from all classes that best approximates the test feature vector. When the class decisions from DTW and the first pass SR classification are different, SR classification is repeated using training samples from these two conflicting classes. Compared to DTW, support vector machines, and an SR classifier without DTW, the proposed classifier achieves the highest classification accuracies of 94% and 89% on manually segmented and automatically segmented phrases, respectively, from unseen Cassin's Vireo individuals, using five training samples per class.

  15. Dynamic time warping and sparse representation classification for birdsong phrase classification using limited training data.

    PubMed

    Tan, Lee N; Alwan, Abeer; Kossan, George; Cody, Martin L; Taylor, Charles E

    2015-03-01

    Annotation of phrases in birdsongs can be helpful to behavioral and population studies. To reduce the need for manual annotation, an automated birdsong phrase classification algorithm for limited data is developed. Limited data occur because of limited recordings or the existence of rare phrases. In this paper, classification of up to 81 phrase classes of Cassin's Vireo is performed using one to five training samples per class. The algorithm involves dynamic time warping (DTW) and two passes of sparse representation (SR) classification. DTW improves the similarity between training and test phrases from the same class in the presence of individual bird differences and phrase segmentation inconsistencies. The SR classifier works by finding a sparse linear combination of training feature vectors from all classes that best approximates the test feature vector. When the class decisions from DTW and the first pass SR classification are different, SR classification is repeated using training samples from these two conflicting classes. Compared to DTW, support vector machines, and an SR classifier without DTW, the proposed classifier achieves the highest classification accuracies of 94% and 89% on manually segmented and automatically segmented phrases, respectively, from unseen Cassin's Vireo individuals, using five training samples per class. PMID:25786922

  16. Gene classification using codon usage and support vector machines.

    PubMed

    Ma, Jianmin; Nguyen, Minh N; Rajapakse, Jagath C

    2009-01-01

    A novel approach for gene classification, which adopts codon usage bias as input feature vector for classification by support vector machines (SVM) is proposed. The DNA sequence is first converted to a 59-dimensional feature vector where each element corresponds to the relative synonymous usage frequency of a codon. As the input to the classifier is independent of sequence length and variance, our approach is useful when the sequences to be classified are of different lengths, a condition that homology-based methods tend to fail. The method is demonstrated by using 1,841 Human Leukocyte Antigen (HLA) sequences which are classified into two major classes: HLA-I and HLA-II; each major class is further subdivided into sub-groups of HLA-I and HLA-II molecules. Using codon usage frequencies, binary SVM achieved accuracy rate of 99.3% for HLA major class classification and multi-class SVM achieved accuracy rates of 99.73% and 98.38% for sub-class classification of HLA-I and HLA-II molecules, respectively. The results show that gene classification based on codon usage bias is consistent with the molecular structures and biological functions of HLA molecules. PMID:19179707

  17. Semi-Supervised Morphosyntactic Classification of Old Icelandic

    PubMed Central

    Urban, Kryztof; Tangherlini, Timothy R.; Vijūnas, Aurelijus; Broadwell, Peter M.

    2014-01-01

    We present IceMorph, a semi-supervised morphosyntactic analyzer of Old Icelandic. In addition to machine-read corpora and dictionaries, it applies a small set of declension prototypes to map corpus words to dictionary entries. A web-based GUI allows expert users to modify and augment data through an online process. A machine learning module incorporates prototype data, edit-distance metrics, and expert feedback to continuously update part-of-speech and morphosyntactic classification. An advantage of the analyzer is its ability to achieve competitive classification accuracy with minimum training data. PMID:25029462

  18. A novel approach to malignant-benign classification of pulmonary nodules by using ensemble learning classifiers.

    PubMed

    Tartar, A; Akan, A; Kilic, N

    2014-01-01

    Computer-aided detection systems can help radiologists to detect pulmonary nodules at an early stage. In this paper, a novel Computer-Aided Diagnosis system (CAD) is proposed for the classification of pulmonary nodules as malignant and benign. The proposed CAD system using ensemble learning classifiers, provides an important support to radiologists at the diagnosis process of the disease, achieves high classification performance. The proposed approach with bagging classifier results in 94.7 %, 90.0 % and 77.8 % classification sensitivities for benign, malignant and undetermined classes (89.5 % accuracy), respectively. PMID:25571029

  19. Fusion of HJ1B and ALOS PALSAR data for land cover classification using machine learning methods

    NASA Astrophysics Data System (ADS)

    Wang, X. Y.; Guo, Y. G.; He, J.; Du, L. T.

    2016-10-01

    Image classification from remote sensing is becoming increasingly urgent for monitoring environmental changes. Exploring effective algorithms to increase classification accuracy is critical. This paper explores the use of multispectral HJ1B and ALOS (Advanced Land Observing Satellite) PALSAR L-band (Phased Array type L-band Synthetic Aperture Radar) for land cover classification using learning-based algorithms. Pixel-based and object-based image analysis approaches for classifying HJ1B data and the HJ1B and ALOS/PALSAR fused-images were compared using two machine learning algorithms, support vector machine (SVM) and random forest (RF), to test which algorithm can achieve the best classification accuracy in arid and semiarid regions. The overall accuracies of the pixel-based (Fused data: 79.0%; HJ1B data: 81.46%) and object-based classifications (Fused data: 80.0%; HJ1B data: 76.9%) were relatively close when using the SVM classifier. The pixel-based classification achieved a high overall accuracy (85.5%) using the RF algorithm for classifying the fused data, whereas the RF classifier using the object-based image analysis produced a lower overall accuracy (70.2%). The study demonstrates that the pixel-based classification utilized fewer variables and performed relatively better than the object-based classification using HJ1B imagery and the fused data. Generally, the integration of the HJ1B and ALOS/PALSAR imagery can improve the overall accuracy of 5.7% using the pixel-based image analysis and RF classifier.

  20. A wavelet transform based feature extraction and classification of cardiac disorder.

    PubMed

    Sumathi, S; Beaulah, H Lilly; Vanithamani, R

    2014-09-01

    This paper approaches an intellectual diagnosis system using hybrid approach of Adaptive Neuro-Fuzzy Inference System (ANFIS) model for classification of Electrocardiogram (ECG) signals. This method is based on using Symlet Wavelet Transform for analyzing the ECG signals and extracting the parameters related to dangerous cardiac arrhythmias. In these particular parameters were used as input of ANFIS classifier, five most important types of ECG signals they are Normal Sinus Rhythm (NSR), Atrial Fibrillation (AF), Pre-Ventricular Contraction (PVC), Ventricular Fibrillation (VF), and Ventricular Flutter (VFLU) Myocardial Ischemia. The inclusion of ANFIS in the complex investigating algorithms yields very interesting recognition and classification capabilities across a broad spectrum of biomedical engineering. The performance of the ANFIS model was evaluated in terms of training performance and classification accuracies. The results give importance to that the proposed ANFIS model illustrates potential advantage in classifying the ECG signals. The classification accuracy of 98.24 % is achieved. PMID:25023652

  1. Pulse oximetry: accuracy of methods of interpreting graphic summaries.

    PubMed

    Lafontaine, V M; Ducharme, F M; Brouillette, R T

    1996-02-01

    Although pulse oximetry has been used to determine the frequency and extent of hemoglobin desaturation during sleep, movement artifact can result in overestimation of desaturation unless valid desaturations can be identified accurately. Therefore, we determined the accuracy of pulmonologists' and technicians' interpretations of graphic displays of desaturation events, derived an objective method for interpreting such events, and validated the method on an independent data set. Eighty-seven randomly selected desaturation events were classified as valid (58) or artifactual (29) based on cardiorespiratory recordings (gold standard) that included pulse waveform and respiratory inductive plethysmography signals. Using oximetry recordings (test method), nine pediatric pulmonologists and three respiratory technicians ("readers") averaged 50 +/- 11% (SD) accuracy for event classification. A single variable, the pulse amplitude modulation range (PAMR) prior to desaturation, performed better in discriminating valid from artifactual events with 76% accuracy (P < 0.05). Following a seminar on oximetry and the use of the PAMR method, the readers' accuracy increased to 73 +/- 2%. In an independent set of 73 apparent desaturation events (74% valid, 26% artifactual), the PAMR method of assessing oximetry graphs yielded 82% accuracy; transcutaneous oxygen tension records confirmed a drop in oxygenation during 49 of 54 (89%) valid desaturation events. In conclusion, the most accurate method (91%) of assessing desaturation events requires recording of the pulse and respiratory waveforms. However, a practical, easy-to-use method of interpreting pulse oximetry recordings achieved 76-82% accuracy, which constitutes a significant improvement from previous subjective interpretations.

  2. Learning ECOC Code Matrix for Multiclass Classification with Application to Glaucoma Diagnosis.

    PubMed

    Bai, Xiaolong; Niwas, Swamidoss Issac; Lin, Weisi; Ju, Bing-Feng; Kwoh, Chee Keong; Wang, Lipo; Sng, Chelvin C; Aquino, Maria C; Chew, Paul T K

    2016-04-01

    Classification of different mechanisms of angle closure glaucoma (ACG) is important for medical diagnosis. Error-correcting output code (ECOC) is an effective approach for multiclass classification. In this study, we propose a new ensemble learning method based on ECOC with application to classification of four ACG mechanisms. The dichotomizers in ECOC are first optimized individually to increase their accuracy and diversity (or interdependence) which is beneficial to the ECOC framework. Specifically, the best feature set is determined for each possible dichotomizer and a wrapper approach is applied to evaluate the classification accuracy of each dichotomizer on the training dataset using cross-validation. The separability of the ECOC codes is maximized by selecting a set of competitive dichotomizers according to a new criterion, in which a regularization term is introduced in consideration of the binary classification performance of each selected dichotomizer. The proposed method is experimentally applied for classifying four ACG mechanisms. The eye images of 152 glaucoma patients are collected by using anterior segment optical coherence tomography (AS-OCT) and then segmented, from which 84 features are extracted. The weighted average classification accuracy of the proposed method is 87.65 % based on the results of leave-one-out cross-validation (LOOCV), which is much better than that of the other existing ECOC methods. The proposed method achieves accurate classification of four ACG mechanisms which is promising to be applied in diagnosis of glaucoma.

  3. Toward fewer EEG channels and better feature extractor of non-motor imagery mental tasks classification for a wheelchair thought controller.

    PubMed

    Chai, Rifai; Ling, Sai Ho; Hunter, Gregory P; Nguyen, Hung T

    2012-01-01

    This paper presents a non-motor imagery tasks classification electroencephalography (EEG) based brain computer interface (BCI) for wheelchair control. It uses only two EEG channels and a better feature extractor to improve the portability and accuracy in the practical system. In addition, two different features extraction methods, power spectral density (PSD) and Hilbert Huang Transform (HHT) energy are compared to find a better method with improved classification accuracy using a Genetic Algorithm (GA) based neural network classifier. The results from five subjects show that using the original eight channels with three tasks, accuracy between 76% and 85% is achieved. With only two channels in combination with the best chosen task using a PSD feature extractor, the accuracy is reduced to between 65% and 79%. However, the HHT based method provides an improved accuracy between 70% and 84% for the classification of three discriminative tasks using two EEG channels.

  4. Land use/cover classification in the Brazilian Amazon using satellite images

    PubMed Central

    Lu, Dengsheng; Batistella, Mateus; Li, Guiying; Moran, Emilio; Hetrick, Scott; Freitas, Corina da Costa; Dutra, Luciano Vieira; Sant’Anna, Sidnei João Siqueira

    2013-01-01

    Land use/cover classification is one of the most important applications in remote sensing. However, mapping accurate land use/cover spatial distribution is a challenge, particularly in moist tropical regions, due to the complex biophysical environment and limitations of remote sensing data per se. This paper reviews experiments related to land use/cover classification in the Brazilian Amazon for a decade. Through comprehensive analysis of the classification results, it is concluded that spatial information inherent in remote sensing data plays an essential role in improving land use/cover classification. Incorporation of suitable textural images into multispectral bands and use of segmentation-based method are valuable ways to improve land use/cover classification, especially for high spatial resolution images. Data fusion of multi-resolution images within optical sensor data is vital for visual interpretation, but may not improve classification performance. In contrast, integration of optical and radar data did improve classification performance when the proper data fusion method was used. Of the classification algorithms available, the maximum likelihood classifier is still an important method for providing reasonably good accuracy, but nonparametric algorithms, such as classification tree analysis, has the potential to provide better results. However, they often require more time to achieve parametric optimization. Proper use of hierarchical-based methods is fundamental for developing accurate land use/cover classification, mainly from historical remotely sensed data. PMID:24353353

  5. Land use/cover classification in the Brazilian Amazon using satellite images.

    PubMed

    Lu, Dengsheng; Batistella, Mateus; Li, Guiying; Moran, Emilio; Hetrick, Scott; Freitas, Corina da Costa; Dutra, Luciano Vieira; Sant'anna, Sidnei João Siqueira

    2012-09-01

    Land use/cover classification is one of the most important applications in remote sensing. However, mapping accurate land use/cover spatial distribution is a challenge, particularly in moist tropical regions, due to the complex biophysical environment and limitations of remote sensing data per se. This paper reviews experiments related to land use/cover classification in the Brazilian Amazon for a decade. Through comprehensive analysis of the classification results, it is concluded that spatial information inherent in remote sensing data plays an essential role in improving land use/cover classification. Incorporation of suitable textural images into multispectral bands and use of segmentation-based method are valuable ways to improve land use/cover classification, especially for high spatial resolution images. Data fusion of multi-resolution images within optical sensor data is vital for visual interpretation, but may not improve classification performance. In contrast, integration of optical and radar data did improve classification performance when the proper data fusion method was used. Of the classification algorithms available, the maximum likelihood classifier is still an important method for providing reasonably good accuracy, but nonparametric algorithms, such as classification tree analysis, has the potential to provide better results. However, they often require more time to achieve parametric optimization. Proper use of hierarchical-based methods is fundamental for developing accurate land use/cover classification, mainly from historical remotely sensed data.

  6. SQL based cardiovascular ultrasound image classification.

    PubMed

    Nandagopalan, S; Suryanarayana, Adiga B; Sudarshan, T S B; Chandrashekar, Dhanalakshmi; Manjunath, C N

    2013-01-01

    This paper proposes a novel method to analyze and classify the cardiovascular ultrasound echocardiographic images using Naïve-Bayesian model via database OLAP-SQL. Efficient data mining algorithms based on tightly-coupled model is used to extract features. Three algorithms are proposed for classification namely Naïve-Bayesian Classifier for Discrete variables (NBCD) with SQL, NBCD with OLAP-SQL, and Naïve-Bayesian Classifier for Continuous variables (NBCC) using OLAP-SQL. The proposed model is trained with 207 patient images containing normal and abnormal categories. Out of the three proposed algorithms, a high classification accuracy of 96.59% was achieved from NBCC which is better than the earlier methods.

  7. Dissimilarity representations in lung parenchyma classification

    NASA Astrophysics Data System (ADS)

    Sørensen, Lauge; de Bruijne, Marleen

    2009-02-01

    A good problem representation is important for a pattern recognition system to be successful. The traditional approach to statistical pattern recognition is feature representation. More specifically, objects are represented by a number of features in a feature vector space, and classifiers are built in this representation. This is also the general trend in lung parenchyma classification in computed tomography (CT) images, where the features often are measures on feature histograms. Instead, we propose to build normal density based classifiers in dissimilarity representations for lung parenchyma classification. This allows for the classifiers to work on dissimilarities between objects, which might be a more natural way of representing lung parenchyma. In this context, dissimilarity is defined between CT regions of interest (ROI)s. ROIs are represented by their CT attenuation histogram and ROI dissimilarity is defined as a histogram dissimilarity measure between the attenuation histograms. In this setting, the full histograms are utilized according to the chosen histogram dissimilarity measure. We apply this idea to classification of different emphysema patterns as well as normal, healthy tissue. Two dissimilarity representation approaches as well as different histogram dissimilarity measures are considered. The approaches are evaluated on a set of 168 CT ROIs using normal density based classifiers all showing good performance. Compared to using histogram dissimilarity directly as distance in a emph{k} nearest neighbor classifier, which achieves a classification accuracy of 92.9%, the best dissimilarity representation based classifier is significantly better with a classification accuracy of 97.0% (text{emph{p" border="0" class="imgtopleft"> = 0.046).

  8. Computer-aided diagnosis system: a Bayesian hybrid classification method.

    PubMed

    Calle-Alonso, F; Pérez, C J; Arias-Nicolás, J P; Martín, J

    2013-10-01

    A novel method to classify multi-class biomedical objects is presented. The method is based on a hybrid approach which combines pairwise comparison, Bayesian regression and the k-nearest neighbor technique. It can be applied in a fully automatic way or in a relevance feedback framework. In the latter case, the information obtained from both an expert and the automatic classification is iteratively used to improve the results until a certain accuracy level is achieved, then, the learning process is finished and new classifications can be automatically performed. The method has been applied in two biomedical contexts by following the same cross-validation schemes as in the original studies. The first one refers to cancer diagnosis, leading to an accuracy of 77.35% versus 66.37%, originally obtained. The second one considers the diagnosis of pathologies of the vertebral column. The original method achieves accuracies ranging from 76.5% to 96.7%, and from 82.3% to 97.1% in two different cross-validation schemes. Even with no supervision, the proposed method reaches 96.71% and 97.32% in these two cases. By using a supervised framework the achieved accuracy is 97.74%. Furthermore, all abnormal cases were correctly classified.

  9. The method of narrow-band audio classification based on universal noise background model

    NASA Astrophysics Data System (ADS)

    Rui, Rui; Bao, Chang-chun

    2013-03-01

    Audio classification is the basis of content-based audio analysis and retrieval. The conventional classification methods mainly depend on feature extraction of audio clip, which certainly increase the time requirement for classification. An approach for classifying the narrow-band audio stream based on feature extraction of audio frame-level is presented in this paper. The audio signals are divided into speech, instrumental music, song with accompaniment and noise using the Gaussian mixture model (GMM). In order to satisfy the demand of actual environment changing, a universal noise background model (UNBM) for white noise, street noise, factory noise and car interior noise is built. In addition, three feature schemes are considered to optimize feature selection. The experimental results show that the proposed algorithm achieves a high accuracy for audio classification, especially under each noise background we used and keep the classification time less than one second.

  10. Radar micro-Doppler based human activity classification for indoor and outdoor environments

    NASA Astrophysics Data System (ADS)

    Zenaldin, Matthew; Narayanan, Ram M.

    2016-05-01

    This paper presents the results of our experimental investigation into how different environments impact the classification of human motion using radar micro-Doppler (MD) signatures. The environments studied include free space, through-thewall, leaf tree foliage, and needle tree foliage. Results on presented on classification of the following three motions: crawling, walking, and jogging. The classification task was designed how to best separate these movements. The human motion data were acquired using a monostatic coherent Doppler radar operating in the C-band at 6.5 GHz from a total of six human subjects. The received signals were analyzed in the time-frequency domain using the Short-time Fourier Transform (STFT) which was used for feature extraction. Classification was performed using a Support Vector Machine (SVM) using a Radial Basis Function (RBF). Classification accuracies in the range 80-90% were achieved to separate the three movements mentioned.

  11. Mammographical mass detection and classification using local seed region growing-spherical wavelet transform (LSRG-SWT) hybrid scheme.

    PubMed

    Görgel, Pelin; Sertbas, Ahmet; Ucan, Osman N

    2013-07-01

    The purpose of this study is to implement accurate methods of detection and classification of benign and malignant breast masses in mammograms. Our new proposed method, which can be used as a diagnostic tool, is denoted Local Seed Region Growing-Spherical Wavelet Transform (LSRG-SWT), and consists of four steps. The first step is homomorphic filtering for enhancement, and the second is detection of the region of interests (ROIs) using a Local Seed Region Growing (LSRG) algorithm, which we developed. The third step incoporates Spherical Wavelet Transform (SWT) and feature extraction. Finally the fourth step is classification, which consists of two sequential components: the 1st classification distinguishes the ROIs as either mass or non-mass and the 2nd classification distinguishes the masses as either benign or malignant using a Support Vector Machine (SVM). The mammograms used in this study were acquired from the hospital of Istanbul University (I.U.) in Turkey and the Mammographic Image Analysis Society (MIAS). The results demonstrate that the proposed scheme LSRG-SWT achieves 96% and 93.59% accuracy in mass/non-mass classification (1st component) and benign/malignant classification (2nd component) respectively when using the I.U. database with k-fold cross validation. The system achieves 94% and 91.67% accuracy in mass/non-mass classification and benign/malignant classification respectively when using the I.U. database as a training set and the MIAS database as a test set with external validation.

  12. Minimum distance classification in remote sensing

    NASA Technical Reports Server (NTRS)

    Wacker, A. G.; Landgrebe, D. A.

    1972-01-01

    The utilization of minimum distance classification methods in remote sensing problems, such as crop species identification, is considered. Literature concerning both minimum distance classification problems and distance measures is reviewed. Experimental results are presented for several examples. The objective of these examples is to: (a) compare the sample classification accuracy of a minimum distance classifier, with the vector classification accuracy of a maximum likelihood classifier, and (b) compare the accuracy of a parametric minimum distance classifier with that of a nonparametric one. Results show the minimum distance classifier performance is 5% to 10% better than that of the maximum likelihood classifier. The nonparametric classifier is only slightly better than the parametric version.

  13. Analyzing thematic maps and mapping for accuracy

    USGS Publications Warehouse

    Rosenfield, G.H.

    1982-01-01

    Two problems which exist while attempting to test the accuracy of thematic maps and mapping are: (1) evaluating the accuracy of thematic content, and (2) evaluating the effects of the variables on thematic mapping. Statistical analysis techniques are applicable to both these problems and include techniques for sampling the data and determining their accuracy. In addition, techniques for hypothesis testing, or inferential statistics, are used when comparing the effects of variables. A comprehensive and valid accuracy test of a classification project, such as thematic mapping from remotely sensed data, includes the following components of statistical analysis: (1) sample design, including the sample distribution, sample size, size of the sample unit, and sampling procedure; and (2) accuracy estimation, including estimation of the variance and confidence limits. Careful consideration must be given to the minimum sample size necessary to validate the accuracy of a given. classification category. The results of an accuracy test are presented in a contingency table sometimes called a classification error matrix. Usually the rows represent the interpretation, and the columns represent the verification. The diagonal elements represent the correct classifications. The remaining elements of the rows represent errors by commission, and the remaining elements of the columns represent the errors of omission. For tests of hypothesis that compare variables, the general practice has been to use only the diagonal elements from several related classification error matrices. These data are arranged in the form of another contingency table. The columns of the table represent the different variables being compared, such as different scales of mapping. The rows represent the blocking characteristics, such as the various categories of classification. The values in the cells of the tables might be the counts of correct classification or the binomial proportions of these counts divided by

  14. 2-Stage Classification Modeling

    1994-11-01

    CIRCUIT2.4 is used to design optimum two-stage classification configurations and operating conditions for energy conservation. It permits simulation of five basic grinding-classification circuits, including one single-stage and four two-stage classification arrangements. Hydrocyclones, spiral classifiers, and sieve band screens can be simulated, and the user may choose the combination of devices for the flowsheet simulation. In addition, the user may select from four classification modeling methods to achieve the goals of a simulation project using themore » most familiar concepts. Circuit performance is modeled based on classification parameters or equipment operating conditions. A modular approach was taken in designing the program, which allows future addition of other models with relatively minor changes.« less

  15. 2-Stage Classification Modeling

    SciTech Connect

    Baltich, L. K.

    1994-11-01

    CIRCUIT2.4 is used to design optimum two-stage classification configurations and operating conditions for energy conservation. It permits simulation of five basic grinding-classification circuits, including one single-stage and four two-stage classification arrangements. Hydrocyclones, spiral classifiers, and sieve band screens can be simulated, and the user may choose the combination of devices for the flowsheet simulation. In addition, the user may select from four classification modeling methods to achieve the goals of a simulation project using the most familiar concepts. Circuit performance is modeled based on classification parameters or equipment operating conditions. A modular approach was taken in designing the program, which allows future addition of other models with relatively minor changes.

  16. Data resolution versus forestry classification and modeling

    NASA Technical Reports Server (NTRS)

    Kan, E. P.; Ball, D. L.; Basu, J. P.; Smelser, R. L.

    1975-01-01

    This paper examines the effects on timber stand computer classification accuracies caused by changes in the resolution of remotely sensed multispectral data. This investigation is valuable, especially for determining optimal sensor and platform designs. Theoretical justification and experimental verification support the finding that classification accuracies for low resolution data could be better than the accuracies for data with higher resolution. The increase in accuracy is constructed as due to the reduction of scene inhomogeneity at lower resolution. The computer classification scheme was a maximum likelihood classifier.

  17. Classification of sporting activities using smartphone accelerometers.

    PubMed

    Mitchell, Edmond; Monaghan, David; O'Connor, Noel E

    2013-04-19

    In this paper we present a framework that allows for the automatic identification of sporting activities using commonly available smartphones. We extract discriminative informational features from smartphone accelerometers using the Discrete Wavelet Transform (DWT). Despite the poor quality of their accelerometers, smartphones were used as capture devices due to their prevalence in today's society. Successful classification on this basis potentially makes the technology accessible to both elite and non-elite athletes. Extracted features are used to train different categories of classifiers. No one classifier family has a reportable direct advantage in activity classification problems to date; thus we examine classifiers from each of the most widely used classifier families. We investigate three classification approaches; a commonly used SVM-based approach, an optimized classification model and a fusion of classifiers. We also investigate the effect of changing several of the DWT input parameters, including mother wavelets, window lengths and DWT decomposition levels. During the course of this work we created a challenging sports activity analysis dataset, comprised of soccer and field-hockey activities. The average maximum F-measure accuracy of 87% was achieved using a fusion of classifiers, which was 6% better than a single classifier model and 23% better than a standard SVM approach.

  18. Classification of Sporting Activities Using Smartphone Accelerometers

    PubMed Central

    Mitchell, Edmond; Monaghan, David; O'Connor, Noel E.

    2013-01-01

    In this paper we present a framework that allows for the automatic identification of sporting activities using commonly available smartphones. We extract discriminative informational features from smartphone accelerometers using the Discrete Wavelet Transform (DWT). Despite the poor quality of their accelerometers, smartphones were used as capture devices due to their prevalence in today's society. Successful classification on this basis potentially makes the technology accessible to both elite and non-elite athletes. Extracted features are used to train different categories of classifiers. No one classifier family has a reportable direct advantage in activity classification problems to date; thus we examine classifiers from each of the most widely used classifier families. We investigate three classification approaches; a commonly used SVM-based approach, an optimized classification model and a fusion of classifiers. We also investigate the effect of changing several of the DWT input parameters, including mother wavelets, window lengths and DWT decomposition levels. During the course of this work we created a challenging sports activity analysis dataset, comprised of soccer and field-hockey activities. The average maximum F-measure accuracy of 87% was achieved using a fusion of classifiers, which was 6% better than a single classifier model and 23% better than a standard SVM approach. PMID:23604031

  19. Semantic Shot Classification in Sports Video

    NASA Astrophysics Data System (ADS)

    Duan, Ling-Yu; Xu, Min; Tian, Qi

    2003-01-01

    In this paper, we present a unified framework for semantic shot classification in sports videos. Unlike previous approaches, which focus on clustering by aggregating shots with similar low-level features, the proposed scheme makes use of domain knowledge of a specific sport to perform a top-down video shot classification, including identification of video shot classes for each sport, and supervised learning and classification of the given sports video with low-level and middle-level features extracted from the sports video. It is observed that for each sport we can predefine a small number of semantic shot classes, about 5~10, which covers 90~95% of sports broadcasting video. With the supervised learning method, we can map the low-level features to middle-level semantic video shot attributes such as dominant object motion (a player), camera motion patterns, and court shape, etc. On the basis of the appropriate fusion of those middle-level shot classes, we classify video shots into the predefined video shot classes, each of which has a clear semantic meaning. The proposed method has been tested over 4 types of sports videos: tennis, basketball, volleyball and soccer. Good classification accuracy of 85~95% has been achieved. With correctly classified sports video shots, further structural and temporal analysis, such as event detection, video skimming, table of content, etc, will be greatly facilitated.

  20. Schizophrenia classification using functional network features

    NASA Astrophysics Data System (ADS)

    Rish, Irina; Cecchi, Guillermo A.; Heuton, Kyle

    2012-03-01

    This paper focuses on discovering statistical biomarkers (features) that are predictive of schizophrenia, with a particular focus on topological properties of fMRI functional networks. We consider several network properties, such as node (voxel) strength, clustering coefficients, local efficiency, as well as just a subset of pairwise correlations. While all types of features demonstrate highly significant statistical differences in several brain areas, and close to 80% classification accuracy, the most remarkable results of 93% accuracy are achieved by using a small subset of only a dozen of most-informative (lowest p-value) correlation features. Our results suggest that voxel-level correlations and functional network features derived from them are highly informative about schizophrenia and can be used as statistical biomarkers for the disease.

  1. An ellipse detection algorithm based on edge classification

    NASA Astrophysics Data System (ADS)

    Yu, Liu; Chen, Feng; Huang, Jianming; Wei, Xiangquan

    2015-12-01

    In order to enhance the speed and accuracy of ellipse detection, an ellipse detection algorithm based on edge classification is proposed. Too many edge points are removed by making edge into point in serialized form and the distance constraint between the edge points. It achieves effective classification by the criteria of the angle between the edge points. And it makes the probability of randomly selecting the edge points falling on the same ellipse greatly increased. Ellipse fitting accuracy is significantly improved by the optimization of the RED algorithm. It uses Euclidean distance to measure the distance from the edge point to the elliptical boundary. Experimental results show that: it can detect ellipse well in case of edge with interference or edges blocking each other. It has higher detecting precision and less time consuming than the RED algorithm.

  2. The Classification of HEp-2 Cell Patterns Using Fractal Descriptor.

    PubMed

    Xu, Rudan; Sun, Yuanyuan; Yang, Zhihao; Song, Bo; Hu, Xiaopeng

    2015-07-01

    Indirect immunofluorescence (IIF) with HEp-2 cells is considered as a powerful, sensitive and comprehensive technique for analyzing antinuclear autoantibodies (ANAs). The automatic classification of the HEp-2 cell images from IIF has played an important role in diagnosis. Fractal dimension can be used on the analysis of image representing and also on the property quantification like texture complexity and spatial occupation. In this study, we apply the fractal theory in the application of HEp-2 cell staining pattern classification, utilizing fractal descriptor firstly in the HEp-2 cell pattern classification with the help of morphological descriptor and pixel difference descriptor. The method is applied to the data set of MIVIA and uses the support vector machine (SVM) classifier. Experimental results show that the fractal descriptor combining with morphological descriptor and pixel difference descriptor makes the precisions of six patterns more stable, all above 50%, achieving 67.17% overall accuracy at best with relatively simple feature vectors.

  3. Classification Options

    ERIC Educational Resources Information Center

    Exceptional Children, 1978

    1978-01-01

    The interview presents opinions of Nicholas Hobbs on the classification of exceptional children, including topics such as ecologically oriented classification systems, the role of parents, and need for revision of teacher preparation programs. (IM)

  4. An Analysis of Scale and Rotation Invariance in the Bag-of-Features Method for Histopathological Image Classification

    PubMed Central

    Raza, S. Hussain; Parry, R. Mitchell; Moffitt, Richard A.; Young, Andrew N.; Wang, May D.

    2016-01-01

    The bag-of-features method has emerged as a useful and flexible tool that can capture medically relevant image characteristics. In this paper, we study the effect of scale and rotation invariance in the bag-of-features framework for Renal Cell Carcinoma subtype classification. We estimated the performance of different features by linear support vector machine over 10 iterations of 3-fold cross validation. For a very heterogeneous dataset labeled by an expert pathologist, we achieve a classification accuracy of 88% with four subtypes. Our study shows that rotation invariance is more important than scale invariance but combining both properties gives better classification performance. PMID:22003685

  5. Interferometric SAR coherence classification utility assessment

    SciTech Connect

    Yocky, D.A.

    1998-03-01

    The classification utility of a dual-antenna interferometric synthetic aperture radar (IFSAR) is explored by comparison of maximum likelihood classification results for synthetic aperture radar (SAR) intensity images and IPSAR intensity and coherence images. The addition of IFSAR coherence improves the overall classification accuracy for classes of trees, water, and fields. A threshold intensity-coherence classifier is also compared to the intensity-only classification results.

  6. Hybrid fNIRS-EEG based classification of auditory and visual perception processes

    PubMed Central

    Putze, Felix; Hesslinger, Sebastian; Tse, Chun-Yu; Huang, YunYing; Herff, Christian; Guan, Cuntai; Schultz, Tanja

    2014-01-01

    For multimodal Human-Computer Interaction (HCI), it is very useful to identify the modalities on which the user is currently processing information. This would enable a system to select complementary output modalities to reduce the user's workload. In this paper, we develop a hybrid Brain-Computer Interface (BCI) which uses Electroencephalography (EEG) and functional Near Infrared Spectroscopy (fNIRS) to discriminate and detect visual and auditory stimulus processing. We describe the experimental setup we used for collection of our data corpus with 12 subjects. On this data, we performed cross-validation evaluation, of which we report accuracy for different classification conditions. The results show that the subject-dependent systems achieved a classification accuracy of 97.8% for discriminating visual and auditory perception processes from each other and a classification accuracy of up to 94.8% for detecting modality-specific processes independently of other cognitive activity. The same classification conditions could also be discriminated in a subject-independent fashion with accuracy of up to 94.6 and 86.7%, respectively. We also look at the contributions of the two signal types and show that the fusion of classifiers using different features significantly increases accuracy. PMID:25477777

  7. Multiclass classification of hemodynamic responses for performance improvement of functional near-infrared spectroscopy-based brain-computer interface

    NASA Astrophysics Data System (ADS)

    Shin, Jaeyoung; Jeong, Jichai

    2014-06-01

    We improved the performance of a functional near-infrared spectroscopy (fNIRS)-based brain-computer interface based on relatively short task duration and multiclass classification. A custom-built eight-channel fNIRS system was used over the motor cortex areas in both hemispheres to measure the hemodynamic responses evoked by four different motor tasks (overt execution of arm lifting and knee extension for both sides) instead of finger tapping. The hemodynamic responses were classified using the naive Bayes classifier. Among the mean, max, slope, variance, and median of the signal amplitude and the time lag of the signal, several signal features are chosen to obtain highest classification accuracy. Ten runs of threefold cross-validation were conducted, which yielded classification accuracies of 87.1%±2.4% to 95.5%±2.4%, 77.5%±1.9% to 92.4%±3.2%, and 73.8%±3.5% to 91.5%±1.4% for the binary, ternary, and quaternary classifications, respectively. Eight seconds of task duration for obtaining sufficient quaternary classification accuracy was suggested. The bit transfer rate per minute (BPM) based on the quaternary classification accuracy was investigated. A BPM can be achieved from 2.81 to 5.40 bits/min.

  8. Remote Sensing Classification Uncertainty: Validating Probabilistic Pixel Level Classification

    NASA Astrophysics Data System (ADS)

    Vrettas, Michail; Cornford, Dan; Bastin, Lucy; Pons, Xavier; Sevillano, Eva; Moré, Gerard; Serra, Pere; Ninyerola, Miquel

    2013-04-01

    There already exists an extensive literature on classification of remotely sensed imagery, and indeed classification more widely, that considers a wide range of probabilistic and non-probabilistic classification methodologies. Although for many probabilistic classification methodologies posterior class probabilities are produced per pixel (observation) these are often not communicated at the pixel level, and typically not validated at the pixel level. Most often the probabilistic classification in converted into a hard classification (of the most probable class) and the accuracy of the resulting classification is reported in terms of a global confusion matrix, or some score derived from this. For applications where classification accuracy is spatially variable and where pixel level estimates of uncertainty can be meaningfully exploited in workflows that propagate uncertainty validating and communicating the pixel level uncertainty opens opportunities for more refined and accountable modelling. In this work we describe our recent work applying and validation of a range of probabilistic classifiers. Using a multi-temporal Landsat data set of the Ebro Delta in Catalonia, which has been carefully radiometrically and geometrically corrected, we present a range of Bayesian classifiers from simple Bayesian linear discriminant analysis to a complex variational Gaussian process based classifier. Field study derived labelled data, classified into 8 classes, which primarily consider land use and the degree of flooding in what is a rice growing region, are used to train the pixel level classifiers. Our focus is not so much on the classification accuracy, but rather the validation of the probabilistic classification made by all methods. We present a range of validation plots and scores, many of which are used for probabilistic weather forecast verification, but are new to remote sensing classification including of course the standard measures of misclassification, but also

  9. Classification of LiDAR Data with Point Based Classification Methods

    NASA Astrophysics Data System (ADS)

    Yastikli, N.; Cetin, Z.

    2016-06-01

    LiDAR is one of the most effective systems for 3 dimensional (3D) data collection in wide areas. Nowadays, airborne LiDAR data is used frequently in various applications such as object extraction, 3D modelling, change detection and revision of maps with increasing point density and accuracy. The classification of the LiDAR points is the first step of LiDAR data processing chain and should be handled in proper way since the 3D city modelling, building extraction, DEM generation, etc. applications directly use the classified point clouds. The different classification methods can be seen in recent researches and most of researches work with the gridded LiDAR point cloud. In grid based data processing of the LiDAR data, the characteristic point loss in the LiDAR point cloud especially vegetation and buildings or losing height accuracy during the interpolation stage are inevitable. In this case, the possible solution is the use of the raw point cloud data for classification to avoid data and accuracy loss in gridding process. In this study, the point based classification possibilities of the LiDAR point cloud is investigated to obtain more accurate classes. The automatic point based approaches, which are based on hierarchical rules, have been proposed to achieve ground, building and vegetation classes using the raw LiDAR point cloud data. In proposed approaches, every single LiDAR point is analyzed according to their features such as height, multi-return, etc. then automatically assigned to the class which they belong to. The use of un-gridded point cloud in proposed point based classification process helped the determination of more realistic rule sets. The detailed parameter analyses have been performed to obtain the most appropriate parameters in the rule sets to achieve accurate classes. The hierarchical rule sets were created for proposed Approach 1 (using selected spatial-based and echo-based features) and Approach 2 (using only selected spatial-based features

  10. Learning regular expressions for clinical text classification

    PubMed Central

    Bui, Duy Duc An; Zeng-Treitler, Qing

    2014-01-01

    Objectives Natural language processing (NLP) applications typically use regular expressions that have been developed manually by human experts. Our goal is to automate both the creation and utilization of regular expressions in text classification. Methods We designed a novel regular expression discovery (RED) algorithm and implemented two text classifiers based on RED. The RED+ALIGN classifier combines RED with an alignment algorithm, and RED+SVM combines RED with a support vector machine (SVM) classifier. Two clinical datasets were used for testing and evaluation: the SMOKE dataset, containing 1091 text snippets describing smoking status; and the PAIN dataset, containing 702 snippets describing pain status. We performed 10-fold cross-validation to calculate accuracy, precision, recall, and F-measure metrics. In the evaluation, an SVM classifier was trained as the control. Results The two RED classifiers achieved 80.9–83.0% in overall accuracy on the two datasets, which is 1.3–3% higher than SVM's accuracy (p<0.001). Similarly, small but consistent improvements have been observed in precision, recall, and F-measure when RED classifiers are compared with SVM alone. More significantly, RED+ALIGN correctly classified many instances that were misclassified by the SVM classifier (8.1–10.3% of the total instances and 43.8–53.0% of SVM's misclassifications). Conclusions Machine-generated regular expressions can be effectively used in clinical text classification. The regular expression-based classifier can be combined with other classifiers, like SVM, to improve classification performance. PMID:24578357

  11. Classification of octet AB-type binary compounds using dynamical charges: A materials informatics perspective

    PubMed Central

    Pilania, G.; Gubernatis, J. E.; Lookman, T.

    2015-01-01

    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. Finally, we compare the out-of-sample classification accuracy achieved by our feature pair with those reported previously. PMID:26631979

  12. 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

  13. Classification of octet AB-type binary compounds using dynamical charges: A materials informatics perspective

    SciTech Connect

    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-sample classification accuracy achieved by our feature pair with those reported previously.

  14. Classification of octet AB-type binary compounds using dynamical charges: A materials informatics perspective

    NASA Astrophysics Data System (ADS)

    Pilania, G.; Gubernatis, J. E.; Lookman, T.

    2015-12-01

    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. Finally, we compare the out-of-sample classification accuracy achieved by our feature pair with those reported previously.

  15. Spaceborne SAR data for land-cover classification and change detection

    NASA Technical Reports Server (NTRS)

    Brisco, B.; Ulaby, F. T.; Dobson, M. C.

    1983-01-01

    Supervised maximum-likelihood classifications of Seasat, SIR-A, and Landsat pixel data demonstrated that SIR-A data provided the most accurate discrimination (72 percent) between five land-cover categories. Spatial averaging of the SAR data improved classification accuracy significantly due to a reduction in both fading and within-field variability. The best multichannel classification accuracy (97.5 percent) was achieved by combining the SIR-A data with two Seasat images (ascending and descending orbits). In addition, semiquantitative analysis of Seasat-A digital data shows that orbital SAR imagery can be successfully used for multitemporal detection of change related to hydrologic and agronomic conditions by using simple machine processing techniques.

  16. Improving image classification in a complex wetland ecosystem through image fusion techniques

    NASA Astrophysics Data System (ADS)

    Kumar, Lalit; Sinha, Priyakant; Taylor, Subhashni

    2014-01-01

    The aim of this study was to evaluate the impact of image fusion techniques on vegetation classification accuracies in a complex wetland system. Fusion of panchromatic (PAN) and multispectral (MS) Quickbird satellite imagery was undertaken using four image fusion techniques: Brovey, hue-saturation-value (HSV), principal components (PC), and Gram-Schmidt (GS) spectral sharpening. These four fusion techniques were compared in terms of their mapping accuracy to a normal MS image using maximum-likelihood classification (MLC) and support vector machine (SVM) methods. Gram-Schmidt fusion technique yielded the highest overall accuracy and kappa value with both MLC (67.5% and 0.63, respectively) and SVM methods (73.3% and 0.68, respectively). This compared favorably with the accuracies achieved using the MS image. Overall, improvements of 4.1%, 3.6%, 5.8%, 5.4%, and 7.2% in overall accuracies were obtained in case of SVM over MLC for Brovey, HSV, GS, PC, and MS images, respectively. Visual and statistical analyses of the fused images showed that the Gram-Schmidt spectral sharpening technique preserved spectral quality much better than the principal component, Brovey, and HSV fused images. Other factors, such as the growth stage of species and the presence of extensive background water in many parts of the study area, had an impact on classification accuracies.

  17. Land Cover Classification from Full-Waveform LIDAR Data Based on Support Vector Machines

    NASA Astrophysics Data System (ADS)

    Zhou, M.; Li, C. R.; Ma, L.; Guan, H. C.

    2016-06-01

    In this study, a land cover classification method based on multi-class Support Vector Machines (SVM) is presented to predict the types of land cover in Miyun area. The obtained backscattered full-waveforms were processed following a workflow of waveform pre-processing, waveform decomposition and feature extraction. The extracted features, which consist of distance, intensity, Full Width at Half Maximum (FWHM) and back scattering cross-section, were corrected and used as attributes for training data to generate the SVM prediction model. The SVM prediction model was applied to predict the types of land cover in Miyun area as ground, trees, buildings and farmland. The classification results of these four types of land covers were obtained based on the ground truth information according to the CCD image data of Miyun area. It showed that the proposed classification algorithm achieved an overall classification accuracy of 90.63%. In order to better explain the SVM classification results, the classification results of SVM method were compared with that of Artificial Neural Networks (ANNs) method and it showed that SVM method could achieve better classification results.

  18. Optimal classification of standoff bioaerosol measurements using evolutionary algorithms

    NASA Astrophysics Data System (ADS)

    Nyhavn, Ragnhild; Moen, Hans J. F.; Farsund, Øystein; Rustad, Gunnar

    2011-05-01

    Early warning systems based on standoff detection of biological aerosols require real-time signal processing of a large quantity of high-dimensional data, challenging the systems efficiency in terms of both computational complexity and classification accuracy. Hence, optimal feature selection is essential in forming a stable and efficient classification system. This involves finding optimal signal processing parameters, characteristic spectral frequencies and other data transformations in large magnitude variable space, stating the need for an efficient and smart search algorithm. Evolutionary algorithms are population-based optimization methods inspired by Darwinian evolutionary theory. These methods focus on application of selection, mutation and recombination on a population of competing solutions and optimize this set by evolving the population of solutions for each generation. We have employed genetic algorithms in the search for optimal feature selection and signal processing parameters for classification of biological agents. The experimental data were achieved with a spectrally resolved lidar based on ultraviolet laser induced fluorescence, and included several releases of 5 common simulants. The genetic algorithm outperform benchmark methods involving analytic, sequential and random methods like support vector machines, Fisher's linear discriminant and principal component analysis, with significantly improved classification accuracy compared to the best classical method.

  19. 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

  20. Automated tissue classification framework for reproducible chronic wound assessment.

    PubMed

    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).

  1. The Accuracy and Reliability of Crowdsource Annotations of Digital Retinal Images

    PubMed Central

    Mitry, Danny; Zutis, Kris; Dhillon, Baljean; Peto, Tunde; Hayat, Shabina; Khaw, Kay-Tee; Morgan, James E.; Moncur, Wendy; Trucco, Emanuele; Foster, Paul J.

    2016-01-01

    Purpose Crowdsourcing is based on outsourcing computationally intensive tasks to numerous individuals in the online community who have no formal training. Our aim was to develop a novel online tool designed to facilitate large-scale annotation of digital retinal images, and to assess the accuracy of crowdsource grading using this tool, comparing it to expert classification. Methods We used 100 retinal fundus photograph images with predetermined disease criteria selected by two experts from a large cohort study. The Amazon Mechanical Turk Web platform was used to drive traffic to our site so anonymous workers could perform a classification and annotation task of the fundus photographs in our dataset after a short training exercise. Three groups were assessed: masters only, nonmasters only and nonmasters with compulsory training. We calculated the sensitivity, specificity, and area under the curve (AUC) of receiver operating characteristic (ROC) plots for all classifications compared to expert grading, and used the Dice coefficient and consensus threshold to assess annotation accuracy. Results In total, we received 5389 annotations for 84 images (excluding 16 training images) in 2 weeks. A specificity and sensitivity of 71% (95% confidence interval [CI], 69%–74%) and 87% (95% CI, 86%–88%) was achieved for all classifications. The AUC in this study for all classifications combined was 0.93 (95% CI, 0.91–0.96). For image annotation, a maximal Dice coefficient (∼0.6) was achieved with a consensus threshold of 0.25. Conclusions This study supports the hypothesis that annotation of abnormalities in retinal images by ophthalmologically naive individuals is comparable to expert annotation. The highest AUC and agreement with expert annotation was achieved in the nonmasters with compulsory training group. Translational Relevance The use of crowdsourcing as a technique for retinal image analysis may be comparable to expert graders and has the potential to deliver

  2. The Accuracy and Reliability of Crowdsource Annotations of Digital Retinal Images

    PubMed Central

    Mitry, Danny; Zutis, Kris; Dhillon, Baljean; Peto, Tunde; Hayat, Shabina; Khaw, Kay-Tee; Morgan, James E.; Moncur, Wendy; Trucco, Emanuele; Foster, Paul J.

    2016-01-01

    Purpose Crowdsourcing is based on outsourcing computationally intensive tasks to numerous individuals in the online community who have no formal training. Our aim was to develop a novel online tool designed to facilitate large-scale annotation of digital retinal images, and to assess the accuracy of crowdsource grading using this tool, comparing it to expert classification. Methods We used 100 retinal fundus photograph images with predetermined disease criteria selected by two experts from a large cohort study. The Amazon Mechanical Turk Web platform was used to drive traffic to our site so anonymous workers could perform a classification and annotation task of the fundus photographs in our dataset after a short training exercise. Three groups were assessed: masters only, nonmasters only and nonmasters with compulsory training. We calculated the sensitivity, specificity, and area under the curve (AUC) of receiver operating characteristic (ROC) plots for all classifications compared to expert grading, and used the Dice coefficient and consensus threshold to assess annotation accuracy. Results In total, we received 5389 annotations for 84 images (excluding 16 training images) in 2 weeks. A specificity and sensitivity of 71% (95% confidence interval [CI], 69%–74%) and 87% (95% CI, 86%–88%) was achieved for all classifications. The AUC in this study for all classifications combined was 0.93 (95% CI, 0.91–0.96). For image annotation, a maximal Dice coefficient (∼0.6) was achieved with a consensus threshold of 0.25. Conclusions This study supports the hypothesis that annotation of abnormalities in retinal images by ophthalmologically naive individuals is comparable to expert annotation. The highest AUC and agreement with expert annotation was achieved in the nonmasters with compulsory training group. Translational Relevance The use of crowdsourcing as a technique for retinal image analysis may be comparable to expert graders and has the potential to deliver

  3. [The Classification of Wheat Varieties Based on Near Infrared Hyperspectral Imaging and Information Fusion].

    PubMed

    Dong, Gao; Guo, Jiani; Wang, Cheng; Chen, Zi-long; Zheng, Ling; Zhu, Da-zhou

    2015-12-01

    Hyperspectral imaging technology has great potential in the identification of crop varieties because it contains both image information and spectral information for the object. But so far most studies only used the spectral information, the image information has not been effectively utilized. In this study, hyperspectral images of single seed of three types including strong gluten wheat, medium gluten wheat, and weak gluten wheat were collected by near infrared hyperspectra imager, 12 morphological characteristics such as length, width, rectangularity, circularity and eccentricity were extracted, the average spectra of endosperm and embryo were acquired by the mask which was created by image segmentation. Partial least squares discriminant analysis (PLADA) and least squares support vector machine (LSSVM) were used to construct the classification model with image information, results showed that the binary classification accuracy between strong gluten wheat and weak gluten wheat could achieve 98%, for strong gluten wheat and medium gluten wheat, it was only 74.22%, which indicated that hyperspectral images could reflect the differences of varieties, but the accuracy might be poor when recognizing the varieties just by image information. Soft independent modeling of class analogy (SIMCA), PLSDA and LSSVM were used to established the classification model with spectral information, the classification effect of endosperm is slightly better than the embryo, it demonstrated that the grain shape could influence the classification accuracy. Then, we fused the spectral and image information, SIMCA, PLSDA and LSSVM were used to established the identification model, the fusion model showed better performance than the individual image model and spectral model, the classification accuracy which used the PLSDA raise from 96.67% to 98.89%, it showed that digging the morphological and spectral characteristics of the hyperspectral image could effectively improve the classification

  4. Evolving point-cloud features for gender classification

    NASA Astrophysics Data System (ADS)

    Keen, Brittany; Fouts, Aaron; Rizki, Mateen; Tamburino, Louis; Mendoza-Schrock, Olga L.

    2011-06-01

    In this paper we explore the use of histogram features extracted from 3D point clouds of human subjects for gender classification. Experiments are conducted using point clouds drawn from the CAESAR anthropometric database provided by the Air Force Research Laboratory (AFRL) Human Effectiveness Directorate and SAE International. This database contains approximately 4400 high resolution LIDAR whole body scans of carefully posed human subjects. Features are extracted from each point cloud by embedding the cloud in series of cylindrical shapes and computing a point count for each cylinder that characterizes a region of the subject. These measurements define rotationally invariant histogram features that are processed by a classifier to label the gender of each subject. Preliminary results using cylinder sizes defined by human experts demonstrate that gender can be predicted with 98% accuracy for the type of high density point cloud found in the CAESAR database. When point cloud densities are reduced to levels that might be obtained using stand-off sensors; gender classification accuracy degrades. We introduce an evolutionary algorithm to optimize the number and size of the cylinders used to define histogram features. The objective of this optimization process is to identify a set of cylindrical features that reduces the error rate when predicting gender from low density point clouds. A wrapper approach is used to interleave feature selection with classifier evaluation to train the evolutionary algorithm. Results of classification accuracy achieved using the evolved features are compared to the baseline feature set defined by human experts.

  5. Towards automatic classification of all WISE sources

    NASA Astrophysics Data System (ADS)

    Kurcz, A.; Bilicki, M.; Solarz, A.; Krupa, M.; Pollo, A.; Małek, K.

    2016-07-01

    Context. The Wide-field Infrared Survey Explorer (WISE) has detected hundreds of millions of sources over the entire sky. Classifying them reliably is, however, a challenging task owing to degeneracies in WISE multicolour space and low levels of detection in its two longest-wavelength bandpasses. Simple colour cuts are often not sufficient; for satisfactory levels of completeness and purity, more sophisticated classification methods are needed. Aims: Here we aim to obtain comprehensive and reliable star, galaxy, and quasar catalogues based on automatic source classification in full-sky WISE data. This means that the final classification will employ only parameters available from WISE itself, in particular those which are reliably measured for the majority of sources. Methods: For the automatic classification we applied a supervised machine learning algorithm, support vector machines (SVM). It requires a training sample with relevant classes already identified, and we chose to use the SDSS spectroscopic dataset (DR10) for that purpose. We tested the performance of two kernels used by the classifier, and determined the minimum number of sources in the training set required to achieve stable classification, as well as the minimum dimension of the parameter space. We also tested SVM classification accuracy as a function of extinction and apparent magnitude. Thus, the calibrated classifier was finally applied to all-sky WISE data, flux-limited to 16 mag (Vega) in the 3.4 μm channel. Results: By calibrating on the test data drawn from SDSS, we first established that a polynomial kernel is preferred over a radial one for this particular dataset. Next, using three classification parameters (W1 magnitude, W1-W2 colour, and a differential aperture magnitude) we obtained very good classification efficiency in all the tests. At the bright end, the completeness for stars and galaxies reaches ~95%, deteriorating to ~80% at W1 = 16 mag, while for quasars it stays at a level of

  6. Measurement of the parity violating 6S-7S transition amplitude in cesium achieved within 2×10-13 atomic-unit accuracy by stimulated-emission detection

    NASA Astrophysics Data System (ADS)

    Guéna, J.; Lintz, M.; Bouchiat, M. A.

    2005-04-01

    We exploit the process of asymmetry amplification by stimulated emission which provides an original method for parity violation (PV) measurements in a highly forbidden atomic transition. The method involves measurements of a chiral, transient, optical gain of a cesium vapor on the 7S-6P3/2 transition, probed after it is excited by an intense, linearly polarized, collinear laser, tuned to resonance for one hyperfine line of the forbidden 6S-7S transition in a longitudinal electric field. We report here a 3.5-fold increase of the one-second-measurement sensitivity and subsequent reduction by a factor of 3.5 of the statistical accuracy compared with our previous result [J. Guéna , Phys. Rev. Lett. 90, 143001 (2003)]. Decisive improvements to the setup include an increased repetition rate, better extinction of the probe beam at the end of the probe pulse, and, for the first time to our knowledge, the following: a polarization-tilt magnifier, quasisuppression of beam reflections at the cell windows, and a Cs cell with electrically conductive windows. We also present real-time tests of systematic effects and consistency checks on the data, as well as a 1% accurate measurement of the electric field seen by the atoms, from atomic signals. PV measurements performed in seven different vapor cells agree within the statistical error. Our present result is compatible with the more precise result of Wood within our present relative statistical accuracy of 2.6%, corresponding to a 2×10-13 atomic-unit uncertainty in E1pv . Theoretical motivations for further measurements are emphasized and we give a brief overview of a recent proposal that would allow the uncertainty to be reduced to the 0.1% level by creating conditions where asymmetry amplification is much greater.

  7. Range and velocity independent classification of humans and animals using a profiling sensor

    NASA Astrophysics Data System (ADS)

    Chari, Srikant; Smith, Forrest; Halford, Carl; Jacobs, Eddie; Brooks, Jason

    2010-04-01

    This paper presents object profile classification results using range and speed independent features from an infrared profiling sensor. The passive infrared profiling sensor was simulated using a LWIR camera. Field data collected near the US-Mexico border to yield profiles of humans and animals is reported. Range and speed independent features based on height and width of the objects were extracted from profiles. The profile features were then used to train and test three classification algorithms to classify objects as humans or animals. The performance of Naïve Bayesian (NB), K-Nearest Neighbors (K-NN), and Support Vector Machines (SVM) are compared based on their classification accuracy. Results indicate that for our data set all three algorithms achieve classification rates of over 98%. The field data is also used to validate our prior data collections from more controlled environments.

  8. Intelligent Classification of Heartbeats for Automated Real-Time ECG Monitoring

    PubMed Central

    Park, Juyoung

    2014-01-01

    Abstract Background: The automatic interpretation of electrocardiography (ECG) data can provide continuous analysis of heart activity, allowing the effective use of wireless devices such as the Holter monitor. Materials and Methods: We propose an intelligent heartbeat monitoring system to detect the possibility of arrhythmia in real time. We detected heartbeats and extracted features such as the QRS complex and P wave from ECG signals using the Pan–Tompkins algorithm, and the heartbeats were then classified into 16 types using a decision tree. Results: We tested the sensitivity, specificity, and accuracy of our system against data from the MIT-BIH Arrhythmia Database. Our system achieved an average accuracy of 97% in heartbeat detection and an average heartbeat classification accuracy of above 96%, which is comparable with the best competing schemes. Conclusions: This work provides a guide to the systematic design of an intelligent classification system for decision support in Holter ECG monitoring. PMID:25010717

  9. 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

  10. A system for heart sounds classification.

    PubMed

    Redlarski, Grzegorz; Gradolewski, Dawid; Palkowski, Aleksander

    2014-01-01

    The future of quick and efficient disease diagnosis lays in the development of reliable non-invasive methods. As for the cardiac diseases - one of the major causes of death around the globe - a concept of an electronic stethoscope equipped with an automatic heart tone identification system appears to be the best solution. Thanks to the advancement in technology, the quality of phonocardiography signals is no longer an issue. However, appropriate algorithms for auto-diagnosis systems of heart diseases that could be capable of distinguishing most of known pathological states have not been yet developed. The main issue is non-stationary character of phonocardiography signals as well as a wide range of distinguishable pathological heart sounds. In this paper a new heart sound classification technique, which might find use in medical diagnostic systems, is presented. It is shown that by combining Linear Predictive Coding coefficients, used for future extraction, with a classifier built upon combining Support Vector Machine and Modified Cuckoo Search algorithm, an improvement in performance of the diagnostic system, in terms of accuracy, complexity and range of distinguishable heart sounds, can be made. The developed system achieved accuracy above 93% for all considered cases including simultaneous identification of twelve different heart sound classes. The respective system is compared with four different major classification methods, proving its reliability. PMID:25393113

  11. Sleep stage classification based on respiratory signal.

    PubMed

    Tataraidze, Alexander; Anishchenko, Lesya; Korostovtseva, Lyudmila; Kooij, Bert Jan; Bochkarev, Mikhail; Sviryaev, Yurii

    2015-01-01

    One of the research tasks, which should be solved to develop a sleep monitor, is sleep stages classification. This paper presents an algorithm for wakefulness, rapid eye movement sleep (REM) and non-REM sleep detection based on a set of 33 features, extracted from respiratory inductive plethysmography signal, and bagging classifier. Furthermore, a few heuristics based on knowledge about normal sleep structure are suggested. We used the data from 29 subjects without sleep-related breathing disorders who underwent a PSG study at a sleep laboratory. Subjects were directed to the PSG study due to suspected sleep disorders. A leave-one-subject-out cross-validation procedure was used for testing the classification performance. The accuracy of 77.85 ± 6.63 and Cohen's kappa of 0.59 ± 0.11 were achieved for the classifier. Using heuristics we increased the accuracy to 80.38 ± 8.32 and the kappa to 0.65 ± 0.13. We conclude that heuristics may improve the automated sleep structure detection based on the analysis of indirect information such as respiration signal and are useful for the development of home sleep monitoring system. PMID:26736273

  12. A System for Heart Sounds Classification

    PubMed Central

    Redlarski, Grzegorz; Gradolewski, Dawid; Palkowski, Aleksander

    2014-01-01

    The future of quick and efficient disease diagnosis lays in the development of reliable non-invasive methods. As for the cardiac diseases – one of the major causes of death around the globe – a concept of an electronic stethoscope equipped with an automatic heart tone identification system appears to be the best solution. Thanks to the advancement in technology, the quality of phonocardiography signals is no longer an issue. However, appropriate algorithms for auto-diagnosis systems of heart diseases that could be capable of distinguishing most of known pathological states have not been yet developed. The main issue is non-stationary character of phonocardiography signals as well as a wide range of distinguishable pathological heart sounds. In this paper a new heart sound classification technique, which might find use in medical diagnostic systems, is presented. It is shown that by combining Linear Predictive Coding coefficients, used for future extraction, with a classifier built upon combining Support Vector Machine and Modified Cuckoo Search algorithm, an improvement in performance of the diagnostic system, in terms of accuracy, complexity and range of distinguishable heart sounds, can be made. The developed system achieved accuracy above 93% for all considered cases including simultaneous identification of twelve different heart sound classes. The respective system is compared with four different major classification methods, proving its reliability. PMID:25393113

  13. Improving Speaking Accuracy through Awareness

    ERIC Educational Resources Information Center

    Dormer, Jan Edwards

    2013-01-01

    Increased English learner accuracy can be achieved by leading students through six stages of awareness. The first three awareness stages build up students' motivation to improve, and the second three provide learners with crucial input for change. The final result is "sustained language awareness," resulting in ongoing…

  14. Automated classification of periodic variable stars detected by the wide-field infrared survey explorer

    SciTech Connect

    Masci, Frank J.; Grillmair, Carl J.; Cutri, Roc M.; Hoffman, Douglas I.

    2014-07-01

    We describe a methodology to classify periodic variable stars identified using photometric time-series measurements constructed from the Wide-field Infrared Survey Explorer (WISE) full-mission single-exposure Source Databases. This will assist in the future construction of a WISE Variable Source Database that assigns variables to specific science classes as constrained by the WISE observing cadence with statistically meaningful classification probabilities. We have analyzed the WISE light curves of 8273 variable stars identified in previous optical variability surveys (MACHO, GCVS, and ASAS) and show that Fourier decomposition techniques can be extended into the mid-IR to assist with their classification. Combined with other periodic light-curve features, this sample is then used to train a machine-learned classifier based on the random forest (RF) method. Consistent with previous classification studies of variable stars in general, the RF machine-learned classifier is superior to other methods in terms of accuracy, robustness against outliers, and relative immunity to features that carry little or redundant class information. For the three most common classes identified by WISE: Algols, RR Lyrae, and W Ursae Majoris type variables, we obtain classification efficiencies of 80.7%, 82.7%, and 84.5% respectively using cross-validation analyses, with 95% confidence intervals of approximately ±2%. These accuracies are achieved at purity (or reliability) levels of 88.5%, 96.2%, and 87.8% respectively, similar to that achieved in previous automated classification studies of periodic variable stars.

  15. Hubble Classification

    NASA Astrophysics Data System (ADS)

    Murdin, P.

    2000-11-01

    A classification scheme for galaxies, devised in its original form in 1925 by Edwin P Hubble (1889-1953), and still widely used today. The Hubble classification recognizes four principal types of galaxy—elliptical, spiral, barred spiral and irregular—and arranges these in a sequence that is called the tuning-fork diagram....

  16. Effective Dimension Reduction Using Sequential Projection Pursuit On Gene Expression Data for Cancer Classification

    SciTech Connect

    Webb-Robertson, Bobbie-Jo M.; Havre, Susan L.

    2004-06-23

    Motiviation: Classification is a powerful tool for uncovering interesting phenomena, for example classes of cancer, in microarray data. Due to the small number of observations (n) in comparison to the number of variables (p), genes, classification on microarray data is challenging. Thus, multivariate dimension reduction techniques are commonly used as a precursor to classification of microarray data; typically this is principal component analysis (PCA) or singular value decomposition (SVD). Since PCA and SVD are concerned with explaining the variance-covariance structure of the data, they may not be the best choice when the between-cluster variance is smaller than the within-cluster variance. Recently an attractive alternative to PCA, sequential projection pursuit (SPP), has been introduced which is designed to elicit clustering tendencies in the data. Thus, in some cases SPP may be more appropriate when performing clustering or classification analysis. Results: We compare the performance of SPP to PCA on two cancer gene expression datasets related to leukemia and colon cancer. Using PCA and SPP to reduce the dimensionality of the data to m<classification accuracy of each method at various levels of . For both datasets SPP achieves higher classification accuracy at low values of m. For example, given m=1 on the two-class case for the leukemia dataset SPP correctly classifies 77% of the observations in comparison to 50% for PCA.

  17. Weapon identification using hierarchical classification of acoustic signatures

    NASA Astrophysics Data System (ADS)

    Khan, Saad; Divakaran, Ajay; Sawhney, Harpreet S.

    2009-05-01

    We apply a unique hierarchical audio classification technique to weapon identification using gunshot analysis. The Audio Classification classifies each audio segment as one of ten weapon classes (e.g., 9mm, 22, shotgun etc.) using lowcomplexity Gaussian Mixture Models (GMM). The first level of hierarchy consists of classification into broad weapons categories such as Rifle, Hand-Gun etc. and the second consists of classification into specific weapons such as 9mm, 357 etc. Our experiments have yielded over 90% classification accuracy at the coarse (rifle-handgun) level of the classification hierarchy and over 85% accuracy at the finer level (weapon category such as 9mm).

  18. Accuracy assessment of land cover/land use classifiers in dry and humid areas of Iran.

    PubMed

    Yousefi, Saleh; Khatami, Reza; Mountrakis, Giorgos; Mirzaee, Somayeh; Pourghasemi, Hamid Reza; Tazeh, Mehdi

    2015-10-01

    Land cover/land use (LCLU) maps are essential inputs for environmental analysis. Remote sensing provides an opportunity to construct LCLU maps of large geographic areas in a timely fashion. Knowing the most accurate classification method to produce LCLU maps based on site characteristics is necessary for the environment managers. The aim of this research is to examine the performance of various classification algorithms for LCLU mapping in dry and humid climates (from June to August). Testing is performed in three case studies from each of the two climates in Iran. The reference dataset of each image was randomly selected from the entire images and was randomly divided into training and validation set. Training sets included 400 pixels, and validation sets included 200 pixels of each LCLU. Results indicate that the support vector machine (SVM) and neural network methods can achieve higher overall accuracy (86.7 and 86.6%) than other examined algorithms, with a slight advantage for the SVM. Dry areas exhibit higher classification difficulty as man-made features often have overlapping spectral responses to soil. A further observation is that spatial segregation and lower mixture of LCLU classes can increase classification overall accuracy.

  19. Experimental study on multi-sub-classifier for land cover classification: a case study in Shangri-La, China

    NASA Astrophysics Data System (ADS)

    Wang, Yan-ying; Wang, Jin-liang; Wang, Ping; Hu, Wen-yin; Su, Shao-hua

    2015-12-01

    High accuracy remote sensed image classification technology is a long-term and continuous pursuit goal of remote sensing applications. In order to evaluate single classification algorithm accuracy, take Landsat TM image as data source, Northwest Yunnan as study area, seven types of land cover classification like Maximum Likelihood Classification has been tested, the results show that: (1)the overall classification accuracy of Maximum Likelihood Classification(MLC), Artificial Neural Network Classification(ANN), Minimum Distance Classification(MinDC) is higher, which is 82.81% and 82.26% and 66.41% respectively; the overall classification accuracy of Parallel Hexahedron Classification(Para), Spectral Information Divergence Classification(SID), Spectral Angle Classification(SAM) is low, which is 37.29%, 38.37, 53.73%, respectively. (2) from each category classification accuracy: although the overall accuracy of the Para is the lowest, it is much higher on grasslands, wetlands, forests, airport land, which is 89.59%, 94.14%, and 89.04%, respectively; the SAM, SID are good at forests classification with higher overall classification accuracy, which is 89.8% and 87.98%, respectively. Although the overall classification accuracy of ANN is very high, the classification accuracy of road, rural residential land and airport land is very low, which is 10.59%, 11% and 11.59% respectively. Other classification methods have their advantages and disadvantages. These results show that, under the same conditions, the same images with different classification methods to classify, there will be a classifier to some features has higher classification accuracy, a classifier to other objects has high classification accuracy, and therefore, we may select multi sub-classifier integration to improve the classification accuracy.

  20. Superordinate Shape Classification Using Natural Shape Statistics

    ERIC Educational Resources Information Center

    Wilder, John; Feldman, Jacob; Singh, Manish

    2011-01-01

    This paper investigates the classification of shapes into broad natural categories such as "animal" or "leaf". We asked whether such coarse classifications can be achieved by a simple statistical classification of the shape skeleton. We surveyed databases of natural shapes, extracting shape skeletons and tabulating their parameters within each…

  1. Automatic Fault Characterization via Abnormality-Enhanced Classification

    SciTech Connect

    Bronevetsky, G; Laguna, I; de Supinski, B R

    2010-12-20

    Enterprise and high-performance computing systems are growing extremely large and complex, employing hundreds to hundreds of thousands of processors and software/hardware stacks built by many people across many organizations. As the growing scale of these machines increases the frequency of faults, system complexity makes these faults difficult to detect and to diagnose. Current system management techniques, which focus primarily on efficient data access and query mechanisms, require system administrators to examine the behavior of various system services manually. Growing system complexity is making this manual process unmanageable: administrators require more effective management tools that can detect faults and help to identify their root causes. System administrators need timely notification when a fault is manifested that includes the type of fault, the time period in which it occurred and the processor on which it originated. Statistical modeling approaches can accurately characterize system behavior. However, the complex effects of system faults make these tools difficult to apply effectively. This paper investigates the application of classification and clustering algorithms to fault detection and characterization. We show experimentally that naively applying these methods achieves poor accuracy. Further, we design novel techniques that combine classification algorithms with information on the abnormality of application behavior to improve detection and characterization accuracy. Our experiments demonstrate that these techniques can detect and characterize faults with 65% accuracy, compared to just 5% accuracy for naive approaches.

  2. Diagnostic classification of intrinsic functional connectivity highlights somatosensory, default mode, and visual regions in autism.

    PubMed

    Chen, Colleen P; Keown, Christopher L; Jahedi, Afrooz; Nair, Aarti; Pflieger, Mark E; Bailey, Barbara A; Müller, Ralph-Axel

    2015-01-01

    Despite consensus on the neurological nature of autism spectrum disorders (ASD), brain biomarkers remain unknown and diagnosis continues to be based on behavioral criteria. Growing evidence suggests that brain abnormalities in ASD occur at the level of interconnected networks; however, previous attempts using functional connectivity data for diagnostic classification have reached only moderate accuracy. We selected 252 low-motion resting-state functional MRI (rs-fMRI) scans from the Autism Brain Imaging Data Exchange (ABIDE) including typically developing (TD) and ASD participants (n = 126 each), matched for age, non-verbal IQ, and head motion. A matrix of functional connectivities between 220 functionally defined regions of interest was used for diagnostic classification, implementing several machine learning tools. While support vector machines in combination with particle swarm optimization and recursive feature elimination performed modestly (with accuracies for validation datasets <70%), diagnostic classification reached a high accuracy of 91% with random forest (RF), a nonparametric ensemble learning method. Among the 100 most informative features (connectivities), for which this peak accuracy was achieved, participation of somatosensory, default mode, visual, and subcortical regions stood out. Whereas some of these findings were expected, given previous findings of default mode abnormalities and atypical visual functioning in ASD, the prominent role of somatosensory regions was remarkable. The finding of peak accuracy for 100 interregional functional connectivities further suggests that brain biomarkers of ASD may be regionally complex and distributed, rather than localized. PMID:26106547

  3. Comparison of Hybrid Classifiers for Crop Classification Using Normalized Difference Vegetation Index Time Series: A Case Study for Major Crops in North Xinjiang, China

    PubMed Central

    Hao, Pengyu; Wang, Li; Niu, Zheng

    2015-01-01

    A range of single classifiers have been proposed to classify crop types using time series vegetation indices, and hybrid classifiers are used to improve discriminatory power. Traditional fusion rules use the product of multi-single classifiers, but that strategy cannot integrate the classification output of machine learning classifiers. In this research, the performance of two hybrid strategies, multiple voting (M-voting) and probabilistic fusion (P-fusion), for crop classification using NDVI time series were tested with different training sample sizes at both pixel and object levels, and two representative counties in north Xinjiang were selected as study area. The single classifiers employed in this research included Random Forest (RF), Support Vector Machine (SVM), and See 5 (C 5.0). The results indicated that classification performance improved (increased the mean overall accuracy by 5%~10%, and reduced standard deviation of overall accuracy by around 1%) substantially with the training sample number, and when the training sample size was small (50 or 100 training samples), hybrid classifiers substantially outperformed single classifiers with higher mean overall accuracy (1%~2%). However, when abundant training samples (4,000) were employed, single classifiers could achieve good classification accuracy, and all classifiers obtained similar performances. Additionally, although object-based classification did not improve accuracy, it resulted in greater visual appeal, especially in study areas with a heterogeneous cropping pattern. PMID:26360597

  4. Comparison of Hybrid Classifiers for Crop Classification Using Normalized Difference Vegetation Index Time Series: A Case Study for Major Crops in North Xinjiang, China.

    PubMed

    Hao, Pengyu; Wang, Li; Niu, Zheng

    2015-01-01

    A range of single classifiers have been proposed to classify crop types using time series vegetation indices, and hybrid classifiers are used to improve discriminatory power. Traditional fusion rules use the product of multi-single classifiers, but that strategy cannot integrate the classification output of machine learning classifiers. In this research, the performance of two hybrid strategies, multiple voting (M-voting) and probabilistic fusion (P-fusion), for crop classification using NDVI time series were tested with different training sample sizes at both pixel and object levels, and two representative counties in north Xinjiang were selected as study area. The single classifiers employed in this research included Random Forest (RF), Support Vector Machine (SVM), and See 5 (C 5.0). The results indicated that classification performance improved (increased the mean overall accuracy by 5%~10%, and reduced standard deviation of overall accuracy by around 1%) substantially with the training sample number, and when the training sample size was small (50 or 100 training samples), hybrid classifiers substantially outperformed single classifiers with higher mean overall accuracy (1%~2%). However, when abundant training samples (4,000) were employed, single classifiers could achieve good classification accuracy, and all classifiers obtained similar performances. Additionally, although object-based classification did not improve accuracy, it resulted in greater visual appeal, especially in study areas with a heterogeneous cropping pattern.

  5. Convolutional neural networks for synthetic aperture radar classification

    NASA Astrophysics Data System (ADS)

    Profeta, Andrew; Rodriguez, Andres; Clouse, H. Scott

    2016-05-01

    For electro-optical object recognition, convolutional neural networks (CNNs) are the state-of-the-art. For large datasets, CNNs are able to learn meaningful features used for classification. However, their application to synthetic aperture radar (SAR) has been limited. In this work we experimented with various CNN architectures on the MSTAR SAR dataset. As the input to the CNN we used the magnitude and phase (2 channels) of the SAR imagery. We used the deep learning toolboxes CAFFE and Torch7. Our results show that we can achieve 93% accuracy on the MSTAR dataset using CNNs.

  6. Single-trial EEG RSVP classification using convolutional neural networks

    NASA Astrophysics Data System (ADS)

    Shamwell, Jared; Lee, Hyungtae; Kwon, Heesung; Marathe, Amar R.; Lawhern, Vernon; Nothwang, William

    2016-05-01

    Traditionally, Brain-Computer Interfaces (BCI) have been explored as a means to return function to paralyzed or otherwise debilitated individuals. An emerging use for BCIs is in human-autonomy sensor fusion where physiological data from healthy subjects is combined with machine-generated information to enhance the capabilities of artificial systems. While human-autonomy fusion of physiological data and computer vision have been shown to improve classification during visual search tasks, to date these approaches have relied on separately trained classification models for each modality. We aim to improve human-autonomy classification performance by developing a single framework that builds codependent models of human electroencephalograph (EEG) and image data to generate fused target estimates. As a first step, we developed a novel convolutional neural network (CNN) architecture and applied it to EEG recordings of subjects classifying target and non-target image presentations during a rapid serial visual presentation (RSVP) image triage task. The low signal-to-noise ratio (SNR) of EEG inherently limits the accuracy of single-trial classification and when combined with the high dimensionality of EEG recordings, extremely large training sets are needed to prevent overfitting and achieve accurate classification from raw EEG data. This paper explores a new deep CNN architecture for generalized multi-class, single-trial EEG classification across subjects. We compare classification performance from the generalized CNN architecture trained across all subjects to the individualized XDAWN, HDCA, and CSP neural classifiers which are trained and tested on single subjects. Preliminary results show that our CNN meets and slightly exceeds the performance of the other classifiers despite being trained across subjects.

  7. A new classification scheme of plastic wastes based upon recycling labels

    SciTech Connect

    Özkan, Kemal; Ergin, Semih; Işık, Şahin; Işıklı, İdil

    2015-01-15

    experimental setup with a camera and homogenous backlighting. Due to the giving global solution for a classification problem, Support Vector Machine (SVM) is selected to achieve the classification task and majority voting technique is used as the decision mechanism. This technique equally weights each classification result and assigns the given plastic object to the class that the most classification results agree on. The proposed classification scheme provides high accuracy rate, and also it is able to run in real-time applications. It can automatically classify the plastic bottle types with approximately 90% recognition accuracy. Besides this, the proposed methodology yields approximately 96% classification rate for the separation of PET or non-PET plastic types. It also gives 92% accuracy for the categorization of non-PET plastic types into HPDE or PP.

  8. Multisensor classification of sedimentary rocks

    NASA Technical Reports Server (NTRS)

    Evans, Diane

    1988-01-01

    A comparison is made between linear discriminant analysis and supervised classification results based on signatures from the Landsat TM, the Thermal Infrared Multispectral Scanner (TIMS), and airborne SAR, alone and combined into extended spectral signatures for seven sedimentary rock units exposed on the margin of the Wind River Basin, Wyoming. Results from a linear discriminant analysis showed that training-area classification accuracies based on the multisensor data were improved an average of 15 percent over TM alone, 24 percent over TIMS alone, and 46 percent over SAR alone, with similar improvement resulting when supervised multisensor classification maps were compared to supervised, individual sensor classification maps. When training area signatures were used to map spectrally similar materials in an adjacent area, the average classification accuracy improved 19 percent using the multisensor data over TM alone, 2 percent over TIMS alone, and 11 percent over SAR alone. It is concluded that certain sedimentary lithologies may be accurately mapped using a single sensor, but classification of a variety of rock types can be improved using multisensor data sets that are sensitive to different characteristics such as mineralogy and surface roughness.

  9. Crop classification using multi-temporal HJ satellite images: case study in Kashgar, Xinjiang

    NASA Astrophysics Data System (ADS)

    Hao, Pengyu; Niu, Zheng; Wang, Li

    2014-11-01

    The HJ satellite constellation, characterized as high temporal resolution (4 day revisit frequency), has high potential to obtain cloud-free images covering all cruel periods for crop classification during growing season. In this paper, three HJ images (in May, July and September) were acquired, the performances of different multi-spectral HJ CCD data combinations for crop classification in Kashgar, Xinjiang were estimated using library for Support Vector Machine (LIBSVM), and ground reference data obtained in 2011 field work were used as training and validation samples. The result showed that multi-temporal HJ data has a potential to classify crops with an overall classification accuracy of 93.77%. Among the three time periods utilized in this research, the image acquired in July achieved the highest overall accuracy (86.98%) because all summer crops were under dense canopy closure. Cotton could be accurately extracted in May image (both user and produce accuracy are above 90%) because of its lower canopy closure compared with spring, the rotate crop (wheat_maize) and winter crop (wheat) at the time period. Then, the July and September combination performed as good as that of all threetime- period combination, which indicated that images obtained at cruel time periods are enough to identify crops, and the additional images improve little on classification accuracy. In addition, multi-temporal NDVI in cruel time periods of the growing season is testified efficient to classify crops with significant phenonlogical variances since they achieved similar overall accuracy to that of multi-temporal multi-spectral combination.

  10. Feature analysis for indoor radar target classification

    NASA Astrophysics Data System (ADS)

    Bufler, Travis D.; Narayanan, Ram M.

    2016-05-01

    This paper analyzes the spectral features from human beings and indoor clutter for building and tuning Support Vector Machines (SVMs) classifiers for the purpose of classifying stationary human targets. The spectral characteristics were obtained through simulations using Finite Difference Time Domain (FDTD) techniques where the radar cross section (RCS) of humans and indoor clutter objects were captured over a wide range of frequencies, polarizations, aspect angles, and materials. Additionally, experimental data was obtained using a vector network analyzer. Two different feature sets for class discrimination are used from the acquired target and clutter RCS spectral data sets. The first feature vectors consist of the raw spectral characteristics, while the second set of feature vectors are statistical features extracted over a set frequency interval. Utilizing variables of frequency and polarization, a SVM classifier can be trained to classify unknown targets as a human or clutter. Classification accuracy over 80% can be effectively achieved given appropriate features.

  11. Classification of radar clutter using neural networks.

    PubMed

    Haykin, S; Deng, C

    1991-01-01

    A classifier that incorporates both preprocessing and postprocessing procedures as well as a multilayer feedforward network (based on the back-propagation algorithm) in its design to distinguish between several major classes of radar returns including weather, birds, and aircraft is described. The classifier achieves an average classification accuracy of 89% on generalization for data collected during a single scan of the radar antenna. The procedures of feature selection for neural network training, the classifier design considerations, the learning algorithm development, the implementation, and the experimental results of the neural clutter classifier, which is simulated on a Warp systolic computer, are discussed. A comparative evaluation of the multilayer neural network with a traditional Bayes classifier is presented.

  12. Collagen morphology and texture analysis: from statistics to classification

    NASA Astrophysics Data System (ADS)

    Mostaço-Guidolin, Leila B.; Ko, Alex C.-T.; Wang, Fei; Xiang, Bo; Hewko, Mark; Tian, Ganghong; Major, Arkady; Shiomi, Masashi; Sowa, Michael G.

    2013-07-01

    In this study we present an image analysis methodology capable of quantifying morphological changes in tissue collagen fibril organization caused by pathological conditions. Texture analysis based on first-order statistics (FOS) and second-order statistics such as gray level co-occurrence matrix (GLCM) was explored to extract second-harmonic generation (SHG) image features that are associated with the structural and biochemical changes of tissue collagen networks. Based on these extracted quantitative parameters, multi-group classification of SHG images was performed. With combined FOS and GLCM texture values, we achieved reliable classification of SHG collagen images acquired from atherosclerosis arteries with >90% accuracy, sensitivity and specificity. The proposed methodology can be applied to a wide range of conditions involving collagen re-modeling, such as in skin disorders, different types of fibrosis and muscular-skeletal diseases affecting ligaments and cartilage.

  13. Energy-Efficient Context Classification With Dynamic Sensor Control

    PubMed Central

    Au, Lawrence K.; Bui, Alex A. T.; Batalin, Maxim A.; Kaiser, William J.

    2016-01-01

    Energy efficiency has been a longstanding design challenge for wearable sensor systems. It is especially crucial in continuous subject state monitoring due to the ongoing need for compact sizes and better sensors. This paper presents an energy-efficient classification algorithm, based on partially observable Markov decision process (POMDP). In every time step, POMDP dynamically selects sensors for classification via a sensor selection policy. The sensor selection problem is formalized as an optimization problem, where the objective is to minimize misclassification cost given some energy budget. State transitions are modeled as a hidden Markov model (HMM), and the corresponding sensor selection policy is represented using a finite-state controller (FSC). To evaluate this framework, sensor data were collected from multiple subjects in their free-living conditions. Relative accuracies and energy reductions from the proposed method are compared against naïve Bayes (always-on) and simple random strategies to validate the relative performance of the algorithm. When the objective is to maintain the same classification accuracy, significant energy reduction is achieved. PMID:23852981

  14. Better physical activity classification using smartphone acceleration sensor.

    PubMed

    Arif, Muhammad; Bilal, Mohsin; Kattan, Ahmed; Ahamed, S Iqbal

    2014-09-01

    Obesity is becoming one of the serious problems for the health of worldwide population. Social interactions on mobile phones and computers via internet through social e-networks are one of the major causes of lack of physical activities. For the health specialist, it is important to track the record of physical activities of the obese or overweight patients to supervise weight loss control. In this study, acceleration sensor present in the smartphone is used to monitor the physical activity of the user. Physical activities including Walking, Jogging, Sitting, Standing, Walking upstairs and Walking downstairs are classified. Time domain features are extracted from the acceleration data recorded by smartphone during different physical activities. Time and space complexity of the whole framework is done by optimal feature subset selection and pruning of instances. Classification results of six physical activities are reported in this paper. Using simple time domain features, 99 % classification accuracy is achieved. Furthermore, attributes subset selection is used to remove the redundant features and to minimize the time complexity of the algorithm. A subset of 30 features produced more than 98 % classification accuracy for the six physical activities.

  15. EFFECTS OF LANDSCAPE CHARACTERISTICS ON LAND-COVER CLASS ACCURACY

    EPA Science Inventory



    Utilizing land-cover data gathered as part of the National Land-Cover Data (NLCD) set accuracy assessment, several logistic regression models were formulated to analyze the effects of patch size and land-cover heterogeneity on classification accuracy. Specific land-cover ...

  16. Assessment Of Accuracies Of Remote-Sensing Maps

    NASA Technical Reports Server (NTRS)

    Card, Don H.; Strong, Laurence L.

    1992-01-01

    Report describes study of accuracies of classifications of picture elements in map derived by digital processing of Landsat-multispectral-scanner imagery of coastal plain of Arctic National Wildlife Refuge. Accuracies of portions of map analyzed with help of statistical sampling procedure called "stratified plurality sampling", in which all picture elements in given cluster classified in stratum to which plurality of them belong.

  17. Remote sensing image classification based on block feature point density analysis and multiple-feature fusion

    NASA Astrophysics Data System (ADS)

    Li, Shijin; Jiang, Yaping; Zhang, Yang; Feng, Jun

    2015-10-01

    With the development of remote sensing (RS) and the related technologies, the resolution of RS images is enhancing. Compared with moderate or low resolution images, high-resolution ones can provide more detailed ground information. However, a variety of terrain has complex spatial distribution. The different objectives of high-resolution images have a variety of features. The effectiveness of these features is not the same, but some of them are complementary. Considering the above information and characteristics, a new method is proposed to classify RS images based on hierarchical fusion of multi-features. Firstly, RS images are pre-classified into two categories in terms of whether feature points are uniformly or non-uniformly distributed. Then, the color histogram and Gabor texture feature are extracted from the uniformly-distributed categories, and the linear spatial pyramid matching using sparse coding (ScSPM) feature is obtained from the non-uniformly-distributed categories. Finally, the classification is performed by two support vector machine classifiers. The experimental results on a large RS image database with 2100 images show that the overall classification accuracy is boosted by 10.1% in comparison with the highest accuracy of single feature classification method. Compared with other multiple-feature fusion methods, the proposed method has achieved the highest classification accuracy on this dataset which has reached 90.1%, and the time complexity of the algorithm is also greatly reduced.

  18. Comparison Between Spectral, Spatial and Polarimetric Classification of Urban and Periurban Landcover Using Temporal Sentinel - 1 Images

    NASA Astrophysics Data System (ADS)

    Roychowdhury, K.

    2016-06-01

    Landcover is the easiest detectable indicator of human interventions on land. Urban and peri-urban areas present a complex combination of landcover, which makes classification challenging. This paper assesses the different methods of classifying landcover using dual polarimetric Sentinel-1 data collected during monsoon (July) and winter (December) months of 2015. Four broad landcover classes such as built up areas, water bodies and wetlands, vegetation and open spaces of Kolkata and its surrounding regions were identified. Polarimetric analyses were conducted on Single Look Complex (SLC) data of the region while ground range detected (GRD) data were used for spectral and spatial classification. Unsupervised classification by means of K-Means clustering used backscatter values and was able to identify homogenous landcovers over the study area. The results produced an overall accuracy of less than 50% for both the seasons. Higher classification accuracy (around 70%) was achieved by adding texture variables as inputs along with the backscatter values. However, the accuracy of classification increased significantly with polarimetric analyses. The overall accuracy was around 80% in Wishart H-A-Alpha unsupervised classification. The method was useful in identifying urban areas due to their double-bounce scattering and vegetated areas, which have more random scattering. Normalized Difference Built-up index (NDBI) and Normalized Difference Vegetation Index (NDVI) obtained from Landsat 8 data over the study area were used to verify vegetation and urban classes. The study compares the accuracies of different methods of classifying landcover using medium resolution SAR data in a complex urban area and suggests that polarimetric analyses present the most accurate results for urban and suburban areas.

  19. ProFold: Protein Fold Classification with Additional Structural Features and a Novel Ensemble Classifier

    PubMed Central

    2016-01-01

    Protein fold classification plays an important role in both protein functional analysis and drug design. The number of proteins in PDB is very large, but only a very small part is categorized and stored in the SCOPe database. Therefore, it is necessary to develop an efficient method for protein fold classification. In recent years, a variety of classification methods have been used in many protein fold classification studies. In this study, we propose a novel classification method called proFold. We import protein tertiary structure in the period of feature extraction and employ a novel ensemble strategy in the period of classifier training. Compared with existing similar ensemble classifiers using the same widely used dataset (DD-dataset), proFold achieves 76.2% overall accuracy. Another two commonly used datasets, EDD-dataset and TG-dataset, are also tested, of which the accuracies are 93.2% and 94.3%, higher than the existing methods. ProFold is available to the public as a web-server.

  20. Multi-level discriminative dictionary learning with application to large scale image classification.

    PubMed

    Shen, Li; Sun, Gang; Huang, Qingming; Wang, Shuhui; Lin, Zhouchen; Wu, Enhua

    2015-10-01

    The sparse coding technique has shown flexibility and capability in image representation and analysis. It is a powerful tool in many visual applications. Some recent work has shown that incorporating the properties of task (such as discrimination for classification task) into dictionary learning is effective for improving the accuracy. However, the traditional supervised dictionary learning methods suffer from high computation complexity when dealing with large number of categories, making them less satisfactory in large scale applications. In this paper, we propose a novel multi-level discriminative dictionary learning method and apply it to large scale image classification. Our method takes advantage of hierarchical category correlation to encode multi-level discriminative information. Each internal node of the category hierarchy is associated with a discriminative dictionary and a classification model. The dictionaries at different layers are learnt to capture the information of different scales. Moreover, each node at lower layers also inherits the dictionary of its parent, so that the categories at lower layers can be described with multi-scale information. The learning of dictionaries and associated classification models is jointly conducted by minimizing an overall tree loss. The experimental results on challenging data sets demonstrate that our approach achieves excellent accuracy and competitive computation cost compared with other sparse coding methods for large scale image classification.

  1. Online Learning for Classification of Alzheimer Disease based on Cortical Thickness and Hippocampal Shape Analysis

    PubMed Central

    Lee, Ga-Young; Kim, Jeonghun; Kim, Ju Han; Kim, Kiwoong

    2014-01-01

    Objectives Mobile healthcare applications are becoming a growing trend. Also, the prevalence of dementia in modern society is showing a steady growing trend. Among degenerative brain diseases that cause dementia, Alzheimer disease (AD) is the most common. The purpose of this study was to identify AD patients using magnetic resonance imaging in the mobile environment. Methods We propose an incremental classification for mobile healthcare systems. Our classification method is based on incremental learning for AD diagnosis and AD prediction using the cortical thickness data and hippocampus shape. We constructed a classifier based on principal component analysis and linear discriminant analysis. We performed initial learning and mobile subject classification. Initial learning is the group learning part in our server. Our smartphone agent implements the mobile classification and shows various results. Results With use of cortical thickness data analysis alone, the discrimination accuracy was 87.33% (sensitivity 96.49% and specificity 64.33%). When cortical thickness data and hippocampal shape were analyzed together, the achieved accuracy was 87.52% (sensitivity 96.79% and specificity 63.24%). Conclusions In this paper, we presented a classification method based on online learning for AD diagnosis by employing both cortical thickness data and hippocampal shape analysis data. Our method was implemented on smartphone devices and discriminated AD patients for normal group. PMID:24627820

  2. ProFold: Protein Fold Classification with Additional Structural Features and a Novel Ensemble Classifier

    PubMed Central

    2016-01-01

    Protein fold classification plays an important role in both protein functional analysis and drug design. The number of proteins in PDB is very large, but only a very small part is categorized and stored in the SCOPe database. Therefore, it is necessary to develop an efficient method for protein fold classification. In recent years, a variety of classification methods have been used in many protein fold classification studies. In this study, we propose a novel classification method called proFold. We import protein tertiary structure in the period of feature extraction and employ a novel ensemble strategy in the period of classifier training. Compared with existing similar ensemble classifiers using the same widely used dataset (DD-dataset), proFold achieves 76.2% overall accuracy. Another two commonly used datasets, EDD-dataset and TG-dataset, are also tested, of which the accuracies are 93.2% and 94.3%, higher than the existing methods. ProFold is available to the public as a web-server. PMID:27660761

  3. Vietnamese Document Representation and Classification

    NASA Astrophysics Data System (ADS)

    Nguyen, Giang-Son; Gao, Xiaoying; Andreae, Peter

    Vietnamese is very different from English and little research has been done on Vietnamese document classification, or indeed, on any kind of Vietnamese language processing, and only a few small corpora are available for research. We created a large Vietnamese text corpus with about 18000 documents, and manually classified them based on different criteria such as topics and styles, giving several classification tasks of different difficulty levels. This paper introduces a new syllable-based document representation at the morphological level of the language for efficient classification. We tested the representation on our corpus with different classification tasks using six classification algorithms and two feature selection techniques. Our experiments show that the new representation is effective for Vietnamese categorization, and suggest that best performance can be achieved using syllable-pair document representation, an SVM with a polynomial kernel as the learning algorithm, and using Information gain and an external dictionary for feature selection.

  4. Efficient Fingercode Classification

    NASA Astrophysics Data System (ADS)

    Sun, Hong-Wei; Law, Kwok-Yan; Gollmann, Dieter; Chung, Siu-Leung; Li, Jian-Bin; Sun, Jia-Guang

    In this paper, we present an efficient fingerprint classification algorithm which is an essential component in many critical security application systems e. g. systems in the e-government and e-finance domains. Fingerprint identification is one of the most important security requirements in homeland security systems such as personnel screening and anti-money laundering. The problem of fingerprint identification involves searching (matching) the fingerprint of a person against each of the fingerprints of all registered persons. To enhance performance and reliability, a common approach is to reduce the search space by firstly classifying the fingerprints and then performing the search in the respective class. Jain et al. proposed a fingerprint classification algorithm based on a two-stage classifier, which uses a K-nearest neighbor classifier in its first stage. The fingerprint classification algorithm is based on the fingercode representation which is an encoding of fingerprints that has been demonstrated to be an effective fingerprint biometric scheme because of its ability to capture both local and global details in a fingerprint image. We enhance this approach by improving the efficiency of the K-nearest neighbor classifier for fingercode-based fingerprint classification. Our research firstly investigates the various fast search algorithms in vector quantization (VQ) and the potential application in fingerprint classification, and then proposes two efficient algorithms based on the pyramid-based search algorithms in VQ. Experimental results on DB1 of FVC 2004 demonstrate that our algorithms can outperform the full search algorithm and the original pyramid-based search algorithms in terms of computational efficiency without sacrificing accuracy.

  5. Classification based on pruning and double covered rule sets for the internet of things applications.

    PubMed

    Li, Shasha; Zhou, Zhongmei; Wang, Weiping

    2014-01-01

    The Internet of things (IOT) is a hot issue in recent years. It accumulates large amounts of data by IOT users, which is a great challenge to mining useful knowledge from IOT. Classification is an effective strategy which can predict the need of users in IOT. However, many traditional rule-based classifiers cannot guarantee that all instances can be covered by at least two classification rules. Thus, these algorithms cannot achieve high accuracy in some datasets. In this paper, we propose a new rule-based classification, CDCR-P (Classification based on the Pruning and Double Covered Rule sets). CDCR-P can induce two different rule sets A and B. Every instance in training set can be covered by at least one rule not only in rule set A, but also in rule set B. In order to improve the quality of rule set B, we take measure to prune the length of rules in rule set B. Our experimental results indicate that, CDCR-P not only is feasible, but also it can achieve high accuracy.

  6. Relative Accuracy Evaluation

    PubMed Central

    Zhang, Yan; Wang, Hongzhi; Yang, Zhongsheng; Li, Jianzhong

    2014-01-01

    The quality of data plays an important role in business analysis and decision making, and data accuracy is an important aspect in data quality. Thus one necessary task for data quality management is to evaluate the accuracy of the data. And in order to solve the problem that the accuracy of the whole data set is low while a useful part may be high, it is also necessary to evaluate the accuracy of the query results, called relative accuracy. However, as far as we know, neither measure nor effective methods for the accuracy evaluation methods are proposed. Motivated by this, for relative accuracy evaluation, we propose a systematic method. We design a relative accuracy evaluation framework for relational databases based on a new metric to measure the accuracy using statistics. We apply the methods to evaluate the precision and recall of basic queries, which show the result's relative accuracy. We also propose the method to handle data update and to improve accuracy evaluation using functional dependencies. Extensive experimental results show the effectiveness and efficiency of our proposed framework and algorithms. PMID:25133752

  7. Subject-Adaptive Real-Time Sleep Stage Classification Based on Conditional Random Field

    PubMed Central

    Luo, Gang; Min, Wanli

    2007-01-01

    Sleep staging is the pattern recognition task of classifying sleep recordings into sleep stages. This task is one of the most important steps in sleep analysis. It is crucial for the diagnosis and treatment of various sleep disorders, and also relates closely to brain-machine interfaces. We report an automatic, online sleep stager using electroencephalogram (EEG) signal based on a recently-developed statistical pattern recognition method, conditional random field, and novel potential functions that have explicit physical meanings. Using sleep recordings from human subjects, we show that the average classification accuracy of our sleep stager almost approaches the theoretical limit and is about 8% higher than that of existing systems. Moreover, for a new subject snew with limited training data Dnew, we perform subject adaptation to improve classification accuracy. Our idea is to use the knowledge learned from old subjects to obtain from Dnew a regulated estimate of CRF’s parameters. Using sleep recordings from human subjects, we show that even without any Dnew, our sleep stager can achieve an average classification accuracy of 70% on snew. This accuracy increases with the size of Dnew and eventually becomes close to the theoretical limit. PMID:18693884

  8. Classification of G-protein coupled receptors at four levels.

    PubMed

    Gao, Qing-Bin; Wang, Zheng-Zhi

    2006-11-01

    G-protein coupled receptors (GPCRs) are transmembrane proteins which via G-proteins initiate some of the important signaling pathways in a cell and are involved in various physiological processes. Thus, computational prediction and classification of GPCRs can supply significant information for the development of novel drugs in pharmaceutical industry. In this paper, a nearest neighbor method has been introduced to discriminate GPCRs from non-GPCRs and subsequently classify GPCRs at four levels on the basis of amino acid composition and dipeptide composition of proteins. Its performance is evaluated on a non-redundant dataset consisted of 1406 GPCRs for six families and 1406 globular proteins using the jackknife test. The present method based on amino acid composition achieved an overall accuracy of 96.4% and Matthew's correlation coefficient (MCC) of 0.930 for correctly picking out the GPCRs from globular proteins. The overall accuracy and MCC were further enhanced to 99.8% and 0.996 by dipeptide composition-based method. On the other hand, the present method has successfully classified 1406 GPCRs into six families with an overall accuracy of 89.6 and 98.8% using amino acid composition and dipeptide composition, respectively. For the subfamily prediction of 1181 GPCRs of rhodopsin-like family, the present method achieved an overall accuracy of 76.7 and 94.5% based on the amino acid composition and dipeptide composition, respectively. Finally, GPCRs belonging to the amine subfamily and olfactory subfamily of rhodopsin-like family were further analyzed at the type level. The overall accuracy of dipeptide composition-based method for the classification of amine type and olfactory type of GPCRs reached 94.5 and 86.9%, respectively, while the overall accuracy of amino acid composition-based method was very low for both subfamilies. In comparison with existing methods in the literature, the present method also displayed great competitiveness. These results demonstrate

  9. Real-Time Fault Classification for Plasma Processes

    PubMed Central

    Yang, Ryan; Chen, Rongshun

    2011-01-01

    Plasma process tools, which usually cost several millions of US dollars, are often used in the semiconductor fabrication etching process. If the plasma process is halted due to some process fault, the productivity will be reduced and the cost will increase. In order to maximize the product/wafer yield and tool productivity, a timely and effective fault process detection is required in a plasma reactor. The classification of fault events can help the users to quickly identify fault processes, and thus can save downtime of the plasma tool. In this work, optical emission spectroscopy (OES) is employed as the metrology sensor for in-situ process monitoring. Splitting into twelve different match rates by spectrum bands, the matching rate indicator in our previous work (Yang, R.; Chen, R.S. Sensors 2010, 10, 5703–5723) is used to detect the fault process. Based on the match data, a real-time classification of plasma faults is achieved by a novel method, developed in this study. Experiments were conducted to validate the novel fault classification. From the experimental results, we may conclude that the proposed method is feasible inasmuch that the overall accuracy rate of the classification for fault event shifts is 27 out of 28 or about 96.4% in success. PMID:22164001

  10. Real-time fault classification for plasma processes.

    PubMed

    Yang, Ryan; Chen, Rongshun

    2011-01-01

    Plasma process tools, which usually cost several millions of US dollars, are often used in the semiconductor fabrication etching process. If the plasma process is halted due to some process fault, the productivity will be reduced and the cost will increase. In order to maximize the product/wafer yield and tool productivity, a timely and effective fault process detection is required in a plasma reactor. The classification of fault events can help the users to quickly identify fault processes, and thus can save downtime of the plasma tool. In this work, optical emission spectroscopy (OES) is employed as the metrology sensor for in-situ process monitoring. Splitting into twelve different match rates by spectrum bands, the matching rate indicator in our previous work (Yang, R.; Chen, R.S. Sensors 2010, 10, 5703-5723) is used to detect the fault process. Based on the match data, a real-time classification of plasma faults is achieved by a novel method, developed in this study. Experiments were conducted to validate the novel fault classification. From the experimental results, we may conclude that the proposed method is feasible inasmuch that the overall accuracy rate of the classification for fault event shifts is 27 out of 28 or about 96.4% in success.

  11. Automatic music genres classification as a pattern recognition problem

    NASA Astrophysics Data System (ADS)

    Ul Haq, Ihtisham; Khan, Fauzia; Sharif, Sana; Shaukat, Arsalan

    2013-12-01

    Music genres are the simplest and effect descriptors for searching music libraries stores or catalogues. The paper compares the results of two automatic music genres classification systems implemented by using two different yet simple classifiers (K-Nearest Neighbor and Naïve Bayes). First a 10-12 second sample is selected and features are extracted from it, and then based on those features results of both classifiers are represented in the form of accuracy table and confusion matrix. An experiment carried out on test 60 taken from middle of a song represents the true essence of its genre as compared to the samples taken from beginning and ending of a song. The novel techniques have achieved an accuracy of 91% and 78% by using Naïve Bayes and KNN classifiers respectively.

  12. Asymptotic accuracy of two-class discrimination

    SciTech Connect

    Ho, T.K.; Baird, H.S.

    1994-12-31

    Poor quality-e.g. sparse or unrepresentative-training data is widely suspected to be one cause of disappointing accuracy of isolated-character classification in modern OCR machines. We conjecture that, for many trainable classification techniques, it is in fact the dominant factor affecting accuracy. To test this, we have carried out a study of the asymptotic accuracy of three dissimilar classifiers on a difficult two-character recognition problem. We state this problem precisely in terms of high-quality prototype images and an explicit model of the distribution of image defects. So stated, the problem can be represented as a stochastic source of an indefinitely long sequence of simulated images labeled with ground truth. Using this sequence, we were able to train all three classifiers to high and statistically indistinguishable asymptotic accuracies (99.9%). This result suggests that the quality of training data was the dominant factor affecting accuracy. The speed of convergence during training, as well as time/space trade-offs during recognition, differed among the classifiers.

  13. Accuracy of Teacher Prediction of Elementary Student Achievement.

    ERIC Educational Resources Information Center

    Gaines, Margie L.; Davis, Margaret

    Whether teachers, in the absence of standardized test scores, can adequately identify students who require remedial services or are at risk of failing a literacy test was studied. During the 1987-88 school year 22 fourth-grade teachers were asked to identify which of 530 students of various ethnic backgrounds would score in the top or bottom…

  14. A new adaptive GMRES algorithm for achieving high accuracy

    SciTech Connect

    Sosonkina, M.; Watson, L.T.; Kapania, R.K.; Walker, H.F.

    1996-12-31

    GMRES(k) is widely used for solving nonsymmetric linear systems. However, it is inadequate either when it converges only for k close to the problem size or when numerical error in the modified Gram-Schmidt process used in the GMRES orthogonalization phase dramatically affects the algorithm performance. An adaptive version of GMRES (k) which tunes the restart value k based on criteria estimating the GMRES convergence rate for the given problem is proposed here. The essence of the adaptive GMRES strategy is to adapt the parameter k to the problem, similar in spirit to how a variable order ODE algorithm tunes the order k. With FORTRAN 90, which provides pointers and dynamic memory management, dealing with the variable storage requirements implied by varying k is not too difficult. The parameter k can be both increased and decreased-an increase-only strategy is described next followed by pseudocode.

  15. Subject Classification.

    ERIC Educational Resources Information Center

    Thompson, Gayle; And Others

    Three newspaper librarians described how they manage the files of newspaper clippings which are a necessary part of their collections. The development of a new subject classification system for the clippings files was outlined. The new subject headings were based on standard subject heading lists and on local need. It was decided to use a computer…

  16. Classifying Classification

    ERIC Educational Resources Information Center

    Novakowski, Janice

    2009-01-01

    This article describes the experience of a group of first-grade teachers as they tackled the science process of classification, a targeted learning objective for the first grade. While the two-year process was not easy and required teachers to teach in a new, more investigation-oriented way, the benefits were great. The project helped teachers and…

  17. Towards Arbitrary Accuracy Inviscid Surface Boundary Conditions

    NASA Technical Reports Server (NTRS)

    Dyson, Rodger W.; Hixon, Ray

    2002-01-01

    Inviscid nonlinear surface boundary conditions are currently limited to third order accuracy in time for non-moving surfaces and actually reduce to first order in time when the surfaces move. For steady-state calculations it may be possible to achieve higher accuracy in space, but high accuracy in time is required for efficient simulation of multiscale unsteady phenomena. A surprisingly simple technique is shown here that can be used to correct the normal pressure derivatives of the flow at a surface on a Cartesian grid so that arbitrarily high order time accuracy is achieved in idealized cases. This work demonstrates that nonlinear high order time accuracy at a solid surface is possible and desirable, but it also shows that the current practice of only correcting the pressure is inadequate.

  18. 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

  19. 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).

  20. Assigning cases to groups using taxometric results: an empirical comparison of classification techniques.

    PubMed

    Ruscio, John

    2009-03-01

    Determining whether individuals belong to different latent classes (taxa) or vary along one or more latent factors (dimensions) has implications for assessment. For example, no instrument can simultaneously maximize the efficiency of categorical and continuous measurement. Methods such as taxometric analysis can test the relative fit of taxonic and dimensional models, but it is not clear how best to assign individuals to groups using taxometric results. The present study compares the performance of two classification techniques-Bayes' theorem and a base-rate technique--across a wide range of data conditions. The base-rate technique achieves greater classification accuracy and a more even balance between sensitivity and specificity. In addition, the base-rate classification technique is easier to implement than Bayes' theorem and is more versatile in that it can be used when the context of assessment requires that cases be classified despite the absence of latent classes.

  1. Fuzzy-logic-based hybrid locomotion mode classification for an active pelvis orthosis: Preliminary results.

    PubMed

    Yuan, Kebin; Parri, Andrea; Yan, Tingfang; Wang, Long; Munih, Marko; Vitiello, Nicola; Wang, Qining

    2015-01-01

    In this paper, we present a fuzzy-logic-based hybrid locomotion mode classification method for an active pelvis orthosis. Locomotion information measured by the onboard hip joint angle sensors and the pressure insoles is used to classify five locomotion modes, including two static modes (sitting, standing still), and three dynamic modes (level-ground walking, ascending stairs, and descending stairs). The proposed method classifies these two kinds of modes first by monitoring the variation of the relative hip joint angle between the two legs within a specific period. Static states are then classified by the time-based absolute hip joint angle. As for dynamic modes, a fuzzy-logic based method is proposed for the classification. Preliminary experimental results with three able-bodied subjects achieve an off-line classification accuracy higher than 99.49%.

  2. Fuzzy-logic-based hybrid locomotion mode classification for an active pelvis orthosis: Preliminary results.

    PubMed

    Yuan, Kebin; Parri, Andrea; Yan, Tingfang; Wang, Long; Munih, Marko; Vitiello, Nicola; Wang, Qining

    2015-01-01

    In this paper, we present a fuzzy-logic-based hybrid locomotion mode classification method for an active pelvis orthosis. Locomotion information measured by the onboard hip joint angle sensors and the pressure insoles is used to classify five locomotion modes, including two static modes (sitting, standing still), and three dynamic modes (level-ground walking, ascending stairs, and descending stairs). The proposed method classifies these two kinds of modes first by monitoring the variation of the relative hip joint angle between the two legs within a specific period. Static states are then classified by the time-based absolute hip joint angle. As for dynamic modes, a fuzzy-logic based method is proposed for the classification. Preliminary experimental results with three able-bodied subjects achieve an off-line classification accuracy higher than 99.49%. PMID:26737144

  3. Development of classification models to detect Salmonella Enteritidis and Salmonella Typhimurium found in poultry carcass rinses by visible-near infrared hyperspectral imaging

    NASA Astrophysics Data System (ADS)

    Seo, Young Wook; Yoon, Seung Chul; Park, Bosoon; Hinton, Arthur; Windham, William R.; Lawrence, Kurt C.

    2013-05-01

    accuracy of all 10 models on a validation set of chicken carcass rinses spiked with SE or ST and incubated on BGS agar plates was 94.45% and 83.73%, without and with PCA for classification, respectively. The best performing classification model on the validation set was QDA without PCA by achieving the classification accuracy of 98.65% (Kappa coefficient=0.98). The overall best performing classification model regardless of using PCA was MD with the classification accuracy of 94.84% (Kappa coefficient=0.88) on the validation set.

  4. A Novel Approach to Probabilistic Biomarker-Based Classification Using Functional Near-Infrared Spectroscopy

    PubMed Central

    Hahn, Tim; Marquand, Andre F; Plichta, Michael M; Ehlis, Ann-Christine; Schecklmann, Martin W; Dresler, Thomas; Jarczok, Tomasz A; Eirich, Elisa; Leonhard, Christine; Reif, Andreas; Lesch, Klaus-Peter; Brammer, Michael J; Mourao-Miranda, Janaina; Fallgatter, Andreas J

    2013-01-01

    Pattern recognition approaches to the analysis of neuroimaging data have brought new applications such as the classification of patients and healthy controls within reach. In our view, the reliance on expensive neuroimaging techniques which are not well tolerated by many patient groups and the inability of most current biomarker algorithms to accommodate information about prior class frequencies (such as a disorder's prevalence in the general population) are key factors limiting practical application. To overcome both limitations, we propose a probabilistic pattern recognition approach based on cheap and easy-to-use multi-channel near-infrared spectroscopy (fNIRS) measurements. We show the validity of our method by applying it to data from healthy controls (n = 14) enabling differentiation between the conditions of a visual checkerboard task. Second, we show that high-accuracy single subject classification of patients with schizophrenia (n = 40) and healthy controls (n = 40) is possible based on temporal patterns of fNIRS data measured during a working memory task. For classification, we integrate spatial and temporal information at each channel to estimate overall classification accuracy. This yields an overall accuracy of 76% which is comparable to the highest ever achieved in biomarker-based classification of patients with schizophrenia. In summary, the proposed algorithm in combination with fNIRS measurements enables the analysis of sub-second, multivariate temporal patterns of BOLD responses and high-accuracy predictions based on low-cost, easy-to-use fNIRS patterns. In addition, our approach can easily compensate for variable class priors, which is highly advantageous in making predictions in a wide range of clinical neuroimaging applications. Hum Brain Mapp, 2013. © 2012 Wiley Periodicals, Inc. PMID:22965654

  5. Spacecraft attitude determination accuracy from mission experience

    NASA Technical Reports Server (NTRS)

    Brasoveanu, D.; Hashmall, J.

    1994-01-01

    This paper summarizes a compilation of attitude determination accuracies attained by a number of satellites supported by the Goddard Space Flight Center Flight Dynamics Facility. The compilation is designed to assist future mission planners in choosing and placing attitude hardware and selecting the attitude determination algorithms needed to achieve given accuracy requirements. The major goal of the compilation is to indicate realistic accuracies achievable using a given sensor complement based on mission experience. It is expected that the use of actual spacecraft experience will make the study especially useful for mission design. A general description of factors influencing spacecraft attitude accuracy is presented. These factors include determination algorithms, inertial reference unit characteristics, and error sources that can affect measurement accuracy. Possible techniques for mitigating errors are also included. Brief mission descriptions are presented with the attitude accuracies attained, grouped by the sensor pairs used in attitude determination. The accuracies for inactive missions represent a compendium of missions report results, and those for active missions represent measurements of attitude residuals. Both three-axis and spin stabilized missions are included. Special emphasis is given to high-accuracy sensor pairs, such as two fixed-head star trackers (FHST's) and fine Sun sensor plus FHST. Brief descriptions of sensor design and mode of operation are included. Also included are brief mission descriptions and plots summarizing the attitude accuracy attained using various sensor complements.

  6. Characterization of a benchmark database for myoelectric movement classification.

    PubMed

    Atzori, Manfredo; Gijsberts, Arjan; Kuzborskij, Ilja; Elsig, Simone; Hager, Anne-Gabrielle Mittaz; Deriaz, Olivier; Castellini, Claudio; Muller, Henning; Caputo, Barbara

    2015-01-01

    In this paper, we characterize the Ninapro database and its use as a benchmark for hand prosthesis evaluation. The database is a publicly available resource that aims to support research on advanced myoelectric hand prostheses. The database is obtained by jointly recording surface electromyography signals from the forearm and kinematics of the hand and wrist while subjects perform a predefined set of actions and postures. Besides describing the acquisition protocol, overall features of the datasets and the processing procedures in detail, we present benchmark classification results using a variety of feature representations and classifiers. Our comparison shows that simple feature representations such as mean absolute value and waveform length can achieve similar performance to the computationally more demanding marginal discrete wavelet transform. With respect to classification methods, the nonlinear support vector machine was found to be the only method consistently achieving high performance regardless of the type of feature representation. Furthermore, statistical analysis of these results shows that classification accuracy is negatively correlated with the subject's Body Mass Index. The analysis and the results described in this paper aim to be a strong baseline for the Ninapro database. Thanks to the Ninapro database (and the characterization described in this paper), the scientific community has the opportunity to converge to a common position on hand movement recognition by surface electromyography, a field capable to strongly affect hand prosthesis capabilities. PMID:25486646

  7. Characterization of a benchmark database for myoelectric movement classification.

    PubMed

    Atzori, Manfredo; Gijsberts, Arjan; Kuzborskij, Ilja; Elsig, Simone; Hager, Anne-Gabrielle Mittaz; Deriaz, Olivier; Castellini, Claudio; Muller, Henning; Caputo, Barbara

    2015-01-01

    In this paper, we characterize the Ninapro database and its use as a benchmark for hand prosthesis evaluation. The database is a publicly available resource that aims to support research on advanced myoelectric hand prostheses. The database is obtained by jointly recording surface electromyography signals from the forearm and kinematics of the hand and wrist while subjects perform a predefined set of actions and postures. Besides describing the acquisition protocol, overall features of the datasets and the processing procedures in detail, we present benchmark classification results using a variety of feature representations and classifiers. Our comparison shows that simple feature representations such as mean absolute value and waveform length can achieve similar performance to the computationally more demanding marginal discrete wavelet transform. With respect to classification methods, the nonlinear support vector machine was found to be the only method consistently achieving high performance regardless of the type of feature representation. Furthermore, statistical analysis of these results shows that classification accuracy is negatively correlated with the subject's Body Mass Index. The analysis and the results described in this paper aim to be a strong baseline for the Ninapro database. Thanks to the Ninapro database (and the characterization described in this paper), the scientific community has the opportunity to converge to a common position on hand movement recognition by surface electromyography, a field capable to strongly affect hand prosthesis capabilities.

  8. Multimodal classification of Alzheimer's disease and mild cognitive impairment.

    PubMed

    Zhang, Daoqiang; Wang, Yaping; Zhou, Luping; Yuan, Hong; Shen, Dinggang

    2011-04-01

    Effective and accurate diagnosis of Alzheimer's disease (AD), as well as its prodromal stage (i.e., mild cognitive impairment (MCI)), has attracted more and more attention recently. So far, multiple biomarkers have been shown to be sensitive to the diagnosis of AD and MCI, i.e., structural MR imaging (MRI) for brain atrophy measurement, functional imaging (e.g., FDG-PET) for hypometabolism quantification, and cerebrospinal fluid (CSF) for quantification of specific proteins. However, most existing research focuses on only a single modality of biomarkers for diagnosis of AD and MCI, although recent studies have shown that different biomarkers may provide complementary information for the diagnosis of AD and MCI. In this paper, we propose to combine three modalities of biomarkers, i.e., MRI, FDG-PET, and CSF biomarkers, to discriminate between AD (or MCI) and healthy controls, using a kernel combination method. Specifically, ADNI baseline MRI, FDG-PET, and CSF data from 51AD patients, 99 MCI patients (including 43 MCI converters who had converted to AD within 18 months and 56 MCI non-converters who had not converted to AD within 18 months), and 52 healthy controls are used for development and validation of our proposed multimodal classification method. In particular, for each MR or FDG-PET image, 93 volumetric features are extracted from the 93 regions of interest (ROIs), automatically labeled by an atlas warping algorithm. For CSF biomarkers, their original values are directly used as features. Then, a linear support vector machine (SVM) is adopted to evaluate the classification accuracy, using a 10-fold cross-validation. As a result, for classifying AD from healthy controls, we achieve a classification accuracy of 93.2% (with a sensitivity of 93% and a specificity of 93.3%) when combining all three modalities of biomarkers, and only 86.5% when using even the best individual modality of biomarkers. Similarly, for classifying MCI from healthy controls, we achieve a

  9. Ebolavirus classification based on natural vectors.

    PubMed

    Zheng, Hui; Yin, Changchuan; Hoang, Tung; He, Rong Lucy; Yang, Jie; Yau, Stephen S-T

    2015-06-01

    According to the WHO, ebolaviruses have resulted in 8818 human deaths in West Africa as of January 2015. To better understand the evolutionary relationship of the ebolaviruses and infer virulence from the relationship, we applied the alignment-free natural vector method to classify the newest ebolaviruses. The dataset includes three new Guinea viruses as well as 99 viruses from Sierra Leone. For the viruses of the family of Filoviridae, both genus label classification and species label classification achieve an accuracy rate of 100%. We represented the relationships among Filoviridae viruses by Unweighted Pair Group Method with Arithmetic Mean (UPGMA) phylogenetic trees and found that the filoviruses can be separated well by three genera. We performed the phylogenetic analysis on the relationship among different species of Ebolavirus by their coding-complete genomes and seven viral protein genes (glycoprotein [GP], nucleoprotein [NP], VP24, VP30, VP35, VP40, and RNA polymerase [L]). The topology of the phylogenetic tree by the viral protein VP24 shows consistency with the variations of virulence of ebolaviruses. The result suggests that VP24 be a pharmaceutical target for treating or preventing ebolaviruses.

  10. ALHAMBRA survey: morphological classification

    NASA Astrophysics Data System (ADS)

    Pović, M.; Huertas-Company, M.; Márquez, I.; Masegosa, J.; Aguerri, J. A. López; Husillos, C.; Molino, A.; Cristóbal-Hornillos, D.

    2015-03-01

    The Advanced Large Homogeneous Area Medium Band Redshift Astronomical (ALHAMBRA) survey is a photometric survey designed to study systematically cosmic evolution and cosmic variance (Moles et al. 2008). It employs 20 continuous medium-band filters (3500 - 9700 Å), plus JHK near-infrared (NIR) bands, which enable measurements of photometric redshifts with good accuracy. ALHAMBRA covers > 4 deg2 in eight discontinuous regions (~ 0.5 deg2 per region), of theseseven fields overlap with other extragalactic, multiwavelength surveys (DEEP2, SDSS, COSMOS, HDF-N, Groth, ELAIS-N1). We detect > 600.000 sources, reaching the depth of R(AB) ~ 25.0, and photometric accuracy of 2-4% (Husillos et al., in prep.). Photometric redshifts are measured using the Bayesian Photometric Redshift (BPZ) code (Benítez et al. 2000), reaching one of the best accuracies up to date of δz/z <= 1.2% (Molino et al., in prep.). To deal with the morphological classification of galaxies in the ALHAMBRA survey (Pović et al., in prep.), we used the galaxy Support Vector Machine code (galSVM; Huertas-Company 2008, 2009), one of the new non-parametric methods for morphological classification, specially useful when dealing with low resolution and high-redshift data. To test the accuracy of our morphological classification we used a sample of 3000 local, visually classified galaxies (Nair & Abraham 2010), moving them to conditions typical of our ALHAMBRA data (taking into account the background, redshift and magnitude distributions, etc.), and measuring their morphology using galSVM. Finally, we measured the morphology of ALHAMBRA galaxies, obtaining for each source seven morphological parameters (two concentration indexes, asymmetry, Gini, M20 moment of light, smoothness, and elongation), probability if the source belongs to early- or late-type, and its error. Comparing ALHAMBRA morph COSMOS/ACS morphology (obtained with the same method) we expect to have qualitative separation in two main morphological

  11. A multiple-point spatially weighted k-NN method for object-based classification

    NASA Astrophysics Data System (ADS)

    Tang, Yunwei; Jing, Linhai; Li, Hui; Atkinson, Peter M.

    2016-10-01

    Object-based classification, commonly referred to as object-based image analysis (OBIA), is now commonly regarded as able to produce more appealing classification maps, often of greater accuracy, than pixel-based classification and its application is now widespread. Therefore, improvement of OBIA using spatial techniques is of great interest. In this paper, multiple-point statistics (MPS) is proposed for object-based classification enhancement in the form of a new multiple-point k-nearest neighbour (k-NN) classification method (MPk-NN). The proposed method first utilises a training image derived from a pre-classified map to characterise the spatial correlation between multiple points of land cover classes. The MPS borrows spatial structures from other parts of the training image, and then incorporates this spatial information, in the form of multiple-point probabilities, into the k-NN classifier. Two satellite sensor images with a fine spatial resolution were selected to evaluate the new method. One is an IKONOS image of the Beijing urban area and the other is a WorldView-2 image of the Wolong mountainous area, in China. The images were object-based classified using the MPk-NN method and several alternatives, including the k-NN, the geostatistically weighted k-NN, the Bayesian method, the decision tree classifier (DTC), and the support vector machine classifier (SVM). It was demonstrated that the new spatial weighting based on MPS can achieve greater classification accuracy relative to the alternatives and it is, thus, recommended as appropriate for object-based classification.

  12. Optimisation of multisource data analysis: an example using evidential reasoning for GIS data classification

    NASA Astrophysics Data System (ADS)

    Peddle, Derek R.; Ferguson, David T.

    2002-02-01

    The classification of integrated data from multiple sources represents a powerful and synergistic approach to deriving important geoscience information from diverse data sets. These data often reside on Geographical Information Systems (GIS) and encompass a variety of sources and properties such as thematic data, remote sensing imagery, topographic data, or environmental map information in raster or vector formats in point, line or polygon representations. Multisource data classification algorithms often require the specification of user-defined parameters to guide data processing, however, these inputs can be numerous or difficult to determine, resulting in less than optimal results. This paper presents three methods for optimising the specification of user-defined inputs based on different levels of empirical testing and computational efficiency: (i) Exhaustive search by recursion, (ii) isolated independent search, and (iii) sequential dependent search. These methods have been implemented in an optimisation software program which is suitable for use with any data classification or analysis algorithm for which user specified inputs are required. In an example application of classifying sub-Arctic mountain permafrost in the Yukon Territory of northern Canada, these optimisation methods were compared in terms of classification accuracy, memory resources and run-time performance using a multisource evidential reasoning classifier, which has been shown to provide improved classification of multisource data compared to neural network, linear discriminant analysis, and maximum likelihood approaches. Using the optimisation software, higher evidential reasoning classification accuracies were achieved without excessive additional computing time. A two-stage approach was recommended for general use to ensure maximum efficiency. It was concluded that these methods are applicable to a wide variety of classification and data analysis algorithms and represent a useful approach

  13. Empirical comparison of color normalization methods for epithelial-stromal classification in H and E images

    PubMed Central

    Sethi, Amit; Sha, Lingdao; Vahadane, Abhishek Ramnath; Deaton, Ryan J.; Kumar, Neeraj; Macias, Virgilia; Gann, Peter H.

    2016-01-01

    Context: Color normalization techniques for histology have not been empirically tested for their utility for computational pathology pipelines. Aims: We compared two contemporary techniques for achieving a common intermediate goal – epithelial-stromal classification. Settings and Design: Expert-annotated regions of epithelium and stroma were treated as ground truth for comparing classifiers on original and color-normalized images. Materials and Methods: Epithelial and stromal regions were annotated on thirty diverse-appearing H and E stained prostate cancer tissue microarray cores. Corresponding sets of thirty images each were generated using the two color normalization techniques. Color metrics were compared for original and color-normalized images. Separate epithelial-stromal classifiers were trained and compared on test images. Main analyses were conducted using a multiresolution segmentation (MRS) approach; comparative analyses using two other classification approaches (convolutional neural network [CNN], Wndchrm) were also performed. Statistical Analysis: For the main MRS method, which relied on classification of super-pixels, the number of variables used was reduced using backward elimination without compromising accuracy, and test - area under the curves (AUCs) were compared for original and normalized images. For CNN and Wndchrm, pixel classification test-AUCs were compared. Results: Khan method reduced color saturation while Vahadane reduced hue variance. Super-pixel-level test-AUC for MRS was 0.010–0.025 (95% confidence interval limits ± 0.004) higher for the two normalized image sets compared to the original in the 10–80 variable range. Improvement in pixel classification accuracy was also observed for CNN and Wndchrm for color-normalized images. Conclusions: Color normalization can give a small incremental benefit when a super-pixel-based classification method is used with features that perform implicit color normalization while the gain is

  14. SherLoc2: a high-accuracy hybrid method for predicting subcellular localization of proteins.

    PubMed

    Briesemeister, Sebastian; Blum, Torsten; Brady, Scott; Lam, Yin; Kohlbacher, Oliver; Shatkay, Hagit

    2009-11-01

    SherLoc2 is a comprehensive high-accuracy subcellular localization prediction system. It is applicable to animal, fungal, and plant proteins and covers all main eukaryotic subcellular locations. SherLoc2 integrates several sequence-based features as well as text-based features. In addition, we incorporate phylogenetic profiles and Gene Ontology (GO) terms derived from the protein sequence to considerably improve the prediction performance. SherLoc2 achieves an overall classification accuracy of up to 93% in 5-fold cross-validation. A novel feature, DiaLoc, allows users to manually provide their current background knowledge by describing a protein in a short abstract which is then used to improve the prediction. SherLoc2 is available both as a free Web service and as a stand-alone version at http://www-bs.informatik.uni-tuebingen.de/Services/SherLoc2.

  15. Gender classification under extended operating conditions

    NASA Astrophysics Data System (ADS)

    Rude, Howard N.; Rizki, Mateen

    2014-06-01

    Gender classification is a critical component of a robust image security system. Many techniques exist to perform gender classification using facial features. In contrast, this paper explores gender classification using body features extracted from clothed subjects. Several of the most effective types of features for gender classification identified in literature were implemented and applied to the newly developed Seasonal Weather And Gender (SWAG) dataset. SWAG contains video clips of approximately 2000 samples of human subjects captured over a period of several months. The subjects are wearing casual business attire and outer garments appropriate for the specific weather conditions observed in the Midwest. The results from a series of experiments are presented that compare the classification accuracy of systems that incorporate various types and combinations of features applied to multiple looks at subjects at different image resolutions to determine a baseline performance for gender classification.

  16. Measuring Diagnoses: ICD Code Accuracy

    PubMed Central

    O'Malley, Kimberly J; Cook, Karon F; Price, Matt D; Wildes, Kimberly Raiford; Hurdle, John F; Ashton, Carol M

    2005-01-01

    Objective To examine potential sources of errors at each step of the described inpatient International Classification of Diseases (ICD) coding process. Data Sources/Study Setting The use of disease codes from the ICD has expanded from classifying morbidity and mortality information for statistical purposes to diverse sets of applications in research, health care policy, and health care finance. By describing a brief history of ICD coding, detailing the process for assigning codes, identifying where errors can be introduced into the process, and reviewing methods for examining code accuracy, we help code users more systematically evaluate code accuracy for their particular applications. Study Design/Methods We summarize the inpatient ICD diagnostic coding process from patient admission to diagnostic code assignment. We examine potential sources of errors at each step and offer code users a tool for systematically evaluating code accuracy. Principle Findings Main error sources along the “patient trajectory” include amount and quality of information at admission, communication among patients and providers, the clinician's knowledge and experience with the illness, and the clinician's attention to detail. Main error sources along the “paper trail” include variance in the electronic and written records, coder training and experience, facility quality-control efforts, and unintentional and intentional coder errors, such as misspecification, unbundling, and upcoding. Conclusions By clearly specifying the code assignment process and heightening their awareness of potential error sources, code users can better evaluate the applicability and limitations of codes for their particular situations. ICD codes can then be used in the most appropriate ways. PMID:16178999

  17. Generalized and Heuristic-Free Feature Construction for Improved Accuracy

    PubMed Central

    Fan, Wei; Zhong, Erheng; Peng, Jing; Verscheure, Olivier; Zhang, Kun; Ren, Jiangtao; Yan, Rong; Yang, Qiang

    2010-01-01

    State-of-the-art learning algorithms accept data in feature vector format as input. Examples belonging to different classes may not always be easy to separate in the original feature space. One may ask: can transformation of existing features into new space reveal significant discriminative information not obvious in the original space? Since there can be infinite number of ways to extend features, it is impractical to first enumerate and then perform feature selection. Second, evaluation of discriminative power on the complete dataset is not always optimal. This is because features highly discriminative on subset of examples may not necessarily be significant when evaluated on the entire dataset. Third, feature construction ought to be automated and general, such that, it doesn't require domain knowledge and its improved accuracy maintains over a large number of classification algorithms. In this paper, we propose a framework to address these problems through the following steps: (1) divide-conquer to avoid exhaustive enumeration; (2) local feature construction and evaluation within subspaces of examples where local error is still high and constructed features thus far still do not predict well; (3) weighting rules based search that is domain knowledge free and has provable performance guarantee. Empirical studies indicate that significant improvement (as much as 9% in accuracy and 28% in AUC) is achieved using the newly constructed features over a variety of inductive learners evaluated against a number of balanced, skewed and high-dimensional datasets. Software and datasets are available from the authors. PMID:21544257

  18. Deep Learning with Convolutional Neural Networks Applied to Electromyography Data: A Resource for the Classification of Movements for Prosthetic Hands

    PubMed Central

    Atzori, Manfredo; Cognolato, Matteo; Müller, Henning

    2016-01-01

    Natural control methods based on surface electromyography (sEMG) and pattern recognition are promising for hand prosthetics. However, the control robustness offered by scientific research is still not sufficient for many real life applications, and commercial prostheses are capable of offering natural control for only a few movements. In recent years deep learning revolutionized several fields of machine learning, including computer vision and speech recognition. Our objective is to test its methods for natural control of robotic hands via sEMG using a large number of intact subjects and amputees. We tested convolutional networks for the classification of an average of 50 hand movements in 67 intact subjects and 11 transradial amputees. The simple architecture of the neural network allowed to make several tests in order to evaluate the effect of pre-processing, layer architecture, data augmentation and optimization. The classification results are compared with a set of classical classification methods applied on the same datasets. The classification accuracy obtained with convolutional neural networks using the proposed architecture is higher than the average results obtained with the classical classification methods, but lower than the results obtained with the best reference methods in our tests. The results show that convolutional neural networks with a very simple architecture can produce accurate results comparable to the average classical classification methods. They show that several factors (including pre-processing, the architecture of the net and the optimization parameters) can be fundamental for the analysis of sEMG data. Larger networks can achieve higher accuracy on computer vision and object recognition tasks. This fact suggests that it may be interesting to evaluate if larger networks can increase sEMG classification accuracy too. PMID:27656140

  19. Deep Learning with Convolutional Neural Networks Applied to Electromyography Data: A Resource for the Classification of Movements for Prosthetic Hands.

    PubMed

    Atzori, Manfredo; Cognolato, Matteo; Müller, Henning

    2016-01-01

    Natural control methods based on surface electromyography (sEMG) and pattern recognition are promising for hand prosthetics. However, the control robustness offered by scientific research is still not sufficient for many real life applications, and commercial prostheses are capable of offering natural control for only a few movements. In recent years deep learning revolutionized several fields of machine learning, including computer vision and speech recognition. Our objective is to test its methods for natural control of robotic hands via sEMG using a large number of intact subjects and amputees. We tested convolutional networks for the classification of an average of 50 hand movements in 67 intact subjects and 11 transradial amputees. The simple architecture of the neural network allowed to make several tests in order to evaluate the effect of pre-processing, layer architecture, data augmentation and optimization. The classification results are compared with a set of classical classification methods applied on the same datasets. The classification accuracy obtained with convolutional neural networks using the proposed architecture is higher than the average results obtained with the classical classification methods, but lower than the results obtained with the best reference methods in our tests. The results show that convolutional neural networks with a very simple architecture can produce accurate results comparable to the average classical classification methods. They show that several factors (including pre-processing, the architecture of the net and the optimization parameters) can be fundamental for the analysis of sEMG data. Larger networks can achieve higher accuracy on computer vision and object recognition tasks. This fact suggests that it may be interesting to evaluate if larger networks can increase sEMG classification accuracy too.

  20. Deep Learning with Convolutional Neural Networks Applied to Electromyography Data: A Resource for the Classification of Movements for Prosthetic Hands.

    PubMed

    Atzori, Manfredo; Cognolato, Matteo; Müller, Henning

    2016-01-01

    Natural control methods based on surface electromyography (sEMG) and pattern recognition are promising for hand prosthetics. However, the control robustness offered by scientific research is still not sufficient for many real life applications, and commercial prostheses are capable of offering natural control for only a few movements. In recent years deep learning revolutionized several fields of machine learning, including computer vision and speech recognition. Our objective is to test its methods for natural control of robotic hands via sEMG using a large number of intact subjects and amputees. We tested convolutional networks for the classification of an average of 50 hand movements in 67 intact subjects and 11 transradial amputees. The simple architecture of the neural network allowed to make several tests in order to evaluate the effect of pre-processing, layer architecture, data augmentation and optimization. The classification results are compared with a set of classical classification methods applied on the same datasets. The classification accuracy obtained with convolutional neural networks using the proposed architecture is higher than the average results obtained with the classical classification methods, but lower than the results obtained with the best reference methods in our tests. The results show that convolutional neural networks with a very simple architecture can produce accurate results comparable to the average classical classification methods. They show that several factors (including pre-processing, the architecture of the net and the optimization parameters) can be fundamental for the analysis of sEMG data. Larger networks can achieve higher accuracy on computer vision and object recognition tasks. This fact suggests that it may be interesting to evaluate if larger networks can increase sEMG classification accuracy too. PMID:27656140

  1. Deep Learning with Convolutional Neural Networks Applied to Electromyography Data: A Resource for the Classification of Movements for Prosthetic Hands

    PubMed Central

    Atzori, Manfredo; Cognolato, Matteo; Müller, Henning

    2016-01-01

    Natural control methods based on surface electromyography (sEMG) and pattern recognition are promising for hand prosthetics. However, the control robustness offered by scientific research is still not sufficient for many real life applications, and commercial prostheses are capable of offering natural control for only a few movements. In recent years deep learning revolutionized several fields of machine learning, including computer vision and speech recognition. Our objective is to test its methods for natural control of robotic hands via sEMG using a large number of intact subjects and amputees. We tested convolutional networks for the classification of an average of 50 hand movements in 67 intact subjects and 11 transradial amputees. The simple architecture of the neural network allowed to make several tests in order to evaluate the effect of pre-processing, layer architecture, data augmentation and optimization. The classification results are compared with a set of classical classification methods applied on the same datasets. The classification accuracy obtained with convolutional neural networks using the proposed architecture is higher than the average results obtained with the classical classification methods, but lower than the results obtained with the best reference methods in our tests. The results show that convolutional neural networks with a very simple architecture can produce accurate results comparable to the average classical classification methods. They show that several factors (including pre-processing, the architecture of the net and the optimization parameters) can be fundamental for the analysis of sEMG data. Larger networks can achieve higher accuracy on computer vision and object recognition tasks. This fact suggests that it may be interesting to evaluate if larger networks can increase sEMG classification accuracy too.

  2. Automatic lung nodule classification with radiomics approach

    NASA Astrophysics Data System (ADS)

    Ma, Jingchen; Wang, Qian; Ren, Yacheng; Hu, Haibo; Zhao, Jun

    2016-03-01

    Lung cancer is the first killer among the cancer deaths. Malignant lung nodules have extremely high mortality while some of the benign nodules don't need any treatment .Thus, the accuracy of diagnosis between benign or malignant nodules diagnosis is necessary. Notably, although currently additional invasive biopsy or second CT scan in 3 months later may help radiologists to make judgments, easier diagnosis approaches are imminently needed. In this paper, we propose a novel CAD method to distinguish the benign and malignant lung cancer from CT images directly, which can not only improve the efficiency of rumor diagnosis but also greatly decrease the pain and risk of patients in biopsy collecting process. Briefly, according to the state-of-the-art radiomics approach, 583 features were used at the first step for measurement of nodules' intensity, shape, heterogeneity and information in multi-frequencies. Further, with Random Forest method, we distinguish the benign nodules from malignant nodules by analyzing all these features. Notably, our proposed scheme was tested on all 79 CT scans with diagnosis data available in The Cancer Imaging Archive (TCIA) which contain 127 nodules and each nodule is annotated by at least one of four radiologists participating in the project. Satisfactorily, this method achieved 82.7% accuracy in classification of malignant primary lung nodules and benign nodules. We believe it would bring much value for routine lung cancer diagnosis in CT imaging and provide improvement in decision-support with much lower cost.

  3. A support vector machine classifier reduces interscanner variation in the HRCT classification of regional disease pattern in diffuse lung disease: Comparison to a Bayesian classifier

    SciTech Connect

    Chang, Yongjun; Lim, Jonghyuck; Kim, Namkug; Seo, Joon Beom; Lynch, David A.

    2013-05-15

    Purpose: To investigate the effect of using different computed tomography (CT) scanners on the accuracy of high-resolution CT (HRCT) images in classifying regional disease patterns in patients with diffuse lung disease, support vector machine (SVM) and Bayesian classifiers were applied to multicenter data. Methods: Two experienced radiologists marked sets of 600 rectangular 20 Multiplication-Sign 20 pixel regions of interest (ROIs) on HRCT images obtained from two scanners (GE and Siemens), including 100 ROIs for each of local patterns of lungs-normal lung and five of regional pulmonary disease patterns (ground-glass opacity, reticular opacity, honeycombing, emphysema, and consolidation). Each ROI was assessed using 22 quantitative features belonging to one of the following descriptors: histogram, gradient, run-length, gray level co-occurrence matrix, low-attenuation area cluster, and top-hat transform. For automatic classification, a Bayesian classifier and a SVM classifier were compared under three different conditions. First, classification accuracies were estimated using data from each scanner. Next, data from the GE and Siemens scanners were used for training and testing, respectively, and vice versa. Finally, all ROI data were integrated regardless of the scanner type and were then trained and tested together. All experiments were performed based on forward feature selection and fivefold cross-validation with 20 repetitions. Results: For each scanner, better classification accuracies were achieved with the SVM classifier than the Bayesian classifier (92% and 82%, respectively, for the GE scanner; and 92% and 86%, respectively, for the Siemens scanner). The classification accuracies were 82%/72% for training with GE data and testing with Siemens data, and 79%/72% for the reverse. The use of training and test data obtained from the HRCT images of different scanners lowered the classification accuracy compared to the use of HRCT images from the same scanner. For

  4. Corpus Callosum MR Image Classification

    NASA Astrophysics Data System (ADS)

    Elsayed, A.; Coenen, F.; Jiang, C.; García-Fiñana, M.; Sluming, V.

    An approach to classifying Magnetic Resonance (MR) image data is described. The specific application is the classification of MRI scan data according to the nature of the corpus callosum, however the approach has more general applicability. A variation of the “spectral segmentation with multi-scale graph decomposition” mechanism is introduced. The result of the segmentation is stored in a quad-tree data structure to which a weighted variation (also developed by the authors) of the gSpan algorithm is applied to identify frequent sub-trees. As a result the images are expressed as a set frequent sub-trees. There may be a great many of these and thus a decision tree based feature reduction technique is applied before classification takes place. The results show that the proposed approach performs both efficiently and effectively, obtaining a classification accuracy of over 95% in the case of the given application.

  5. Classification of EEG signals using a multiple kernel learning support vector machine.

    PubMed

    Li, Xiaoou; Chen, Xun; Yan, Yuning; Wei, Wenshi; Wang, Z Jane

    2014-07-17

    In this study, a multiple kernel learning support vector machine algorithm is proposed for the identification of EEG signals including mental and cognitive tasks, which is a key component in EEG-based brain computer interface (BCI) systems. The presented BCI approach included three stages: (1) a pre-processing step was performed to improve the general signal quality of the EEG; (2) the features were chosen, including wavelet packet entropy and Granger causality, respectively; (3) a multiple kernel learning support vector machine (MKL-SVM) based on a gradient descent optimization algorithm was investigated to classify EEG signals, in which the kernel was defined as a linear combination of polynomial kernels and radial basis function kernels. Experimental results showed that the proposed method provided better classification performance compared with the SVM based on a single kernel. For mental tasks, the average accuracies for 2-class, 3-class, 4-class, and 5-class classifications were 99.20%, 81.25%, 76.76%, and 75.25% respectively. Comparing stroke patients with healthy controls using the proposed algorithm, we achieved the average classification accuracies of 89.24% and 80.33% for 0-back and 1-back tasks respectively. Our results indicate that the proposed approach is promising for implementing human-computer interaction (HCI), especially for mental task classification and identifying suitable brain impairment candidates.

  6. Classification of mammographic masses using support vector machines and Bayesian networks

    NASA Astrophysics Data System (ADS)

    Samulski, Maurice; Karssemeijer, Nico; Lucas, Peter; Groot, Perry

    2007-03-01

    In this paper, we compare two state-of-the-art classification techniques characterizing masses as either benign or malignant, using a dataset consisting of 271 cases (131 benign and 140 malignant), containing both a MLO and CC view. For suspect regions in a digitized mammogram, 12 out of 81 calculated image features have been selected for investigating the classification accuracy of support vector machines (SVMs) and Bayesian networks (BNs). Additional techniques for improving their performance were included in their comparison: the Manly transformation for achieving a normal distribution of image features and principal component analysis (PCA) for reducing our high-dimensional data. The performance of the classifiers were evaluated with Receiver Operating Characteristics (ROC) analysis. The classifiers were trained and tested using a k-fold cross-validation test method (k=10). It was found that the area under the ROC curve (A z) of the BN increased significantly (p=0.0002) using the Manly transformation, from A z = 0.767 to A z = 0.795. The Manly transformation did not result in a significant change for SVMs. Also the difference between SVMs and BNs using the transformed dataset was not statistically significant (p=0.78). Applying PCA resulted in an improvement in classification accuracy of the naive Bayesian classifier, from A z = 0.767 to A z = 0.786. The difference in classification performance between BNs and SVMs after applying PCA was small and not statistically significant (p=0.11).

  7. Activity classification based on inertial and barometric pressure sensors at different anatomical locations.

    PubMed

    Moncada-Torres, A; Leuenberger, K; Gonzenbach, R; Luft, A; Gassert, R

    2014-07-01

    Miniature, wearable sensor modules are a promising technology to monitor activities of daily living (ADL) over extended periods of time. To assure both user compliance and meaningful results, the selection and placement site of sensors requires careful consideration. We investigated these aspects for the classification of 16 ADL in 6 healthy subjects under laboratory conditions using ReSense, our custom-made inertial measurement unit enhanced with a barometric pressure sensor used to capture activity-related altitude changes. Subjects wore a module on each wrist and ankle, and one on the trunk. Activities comprised whole body movements as well as gross and dextrous upper-limb activities. Wrist-module data outperformed the other locations for the three activity groups. Specifically, overall classification accuracy rates of almost 93% and more than 95% were achieved for the repeated holdout and user-specific validation methods, respectively, for all 16 activities. Including the altitude profile resulted in a considerable improvement of up to 20% in the classification accuracy for stair ascent and descent. The gyroscopes provided no useful information for activity classification under this scheme. The proposed sensor setting could allow for robust long-term activity monitoring with high compliance in different patient populations.

  8. Keratin protein property based classification of mammals and non-mammals using machine learning techniques.

    PubMed

    Banerjee, Amit Kumar; Ravi, Vadlamani; Murty, U S N; Shanbhag, Anirudh P; Prasanna, V Lakshmi

    2013-08-01

    Keratin protein is ubiquitous in most vertebrates and invertebrates, and has several important cellular and extracellular functions that are related to survival and protection. Keratin function has played a significant role in the natural selection of an organism. Hence, it acts as a marker of evolution. Much information about an organism and its evolution can therefore be obtained by investigating this important protein. In the present study, Keratin sequences were extracted from public data repositories and various important sequential, structural and physicochemical properties were computed and used for preparing the dataset. The dataset containing two classes, namely mammals (Class-1) and non-mammals (Class-0), was prepared, and rigorous classification analysis was performed. To reduce the complexity of the dataset containing 56 parameters and to achieve improved accuracy, feature selection was done using the t-statistic. The 20 best features (parameters) were selected for further classification analysis using computational algorithms which included SVM, KNN, Neural Network, Logistic regression, Meta-modeling, Tree Induction, Rule Induction, Discriminant analysis and Bayesian Modeling. Statistical methods were used to evaluate the output. Logistic regression was found to be the most effective algorithm for classification, with greater than 96% accuracy using a 10-fold cross validation analysis. KNN, SVM and Rule Induction algorithms also were found to be efficacious for classification.

  9. GEOSPATIAL DATA ACCURACY ASSESSMENT

    EPA Science Inventory

    The development of robust accuracy assessment methods for the validation of spatial data represent's a difficult scientific challenge for the geospatial science community. The importance and timeliness of this issue is related directly to the dramatic escalation in the developmen...

  10. 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

    2016-01-01

    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

  11. 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

  12. 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.

  13. Accuracy potentials for large space antenna structures

    NASA Technical Reports Server (NTRS)

    Hedgepeth, J. M.

    1980-01-01

    The relationships among materials selection, truss design, and manufacturing techniques in the interest of surface accuracies for large space antennas are discussed. Among the antenna configurations considered are: tetrahedral truss, pretensioned truss, and geodesic dome and radial rib structures. Comparisons are made of the accuracy achievable by truss and dome structure types for a wide variety of diameters, focal lengths, and wavelength of radiated signal, taking into account such deforming influences as solar heating-caused thermal transients and thermal gradients.

  14. The use of airborne hyperspectral data for tree species classification in a species-rich Central European forest area

    NASA Astrophysics Data System (ADS)

    Richter, Ronny; Reu, Björn; Wirth, Christian; Doktor, Daniel; Vohland, Michael

    2016-10-01

    The success of remote sensing approaches to assess tree species diversity in a heterogeneously mixed forest stand depends on the availability of both appropriate data and suitable classification algorithms. To separate the high number of in total ten broadleaf tree species in a small structured floodplain forest, the Leipzig Riverside Forest, we introduce a majority based classification approach for Discriminant Analysis based on Partial Least Squares (PLS-DA), which was tested against Random Forest (RF) and Support Vector Machines (SVM). The classifier performance was tested on different sets of airborne hyperspectral image data (AISA DUAL) that were acquired on single dates in August and September and also stacked to a composite product. Shadowed gaps and shadowed crown parts were eliminated via spectral mixture analysis (SMA) prior to the pixel-based classification. Training and validation sets were defined spectrally with the conditioned Latin hypercube method as a stratified random sampling procedure. In the validation, PLS-DA consistently outperformed the RF and SVM approaches on all datasets. The additional use of spectral variable selection (CARS, "competitive adaptive reweighted sampling") combined with PLS-DA further improved classification accuracies. Up to 78.4% overall accuracy was achieved for the stacked dataset. The image recorded in August provided slightly higher accuracies than the September image, regardless of the applied classifier.

  15. Unsupervised classification of operator workload from brain signals

    NASA Astrophysics Data System (ADS)

    Schultze-Kraft, Matthias; Dähne, Sven; Gugler, Manfred; Curio, Gabriel; Blankertz, Benjamin

    2016-06-01

    Objective. In this study we aimed for the classification of operator workload as it is expected in many real-life workplace environments. We explored brain-signal based workload predictors that differ with respect to the level of label information required for training, including entirely unsupervised approaches. Approach. Subjects executed a task on a touch screen that required continuous effort of visual and motor processing with alternating difficulty. We first employed classical approaches for workload state classification that operate on the sensor space of EEG and compared those to the performance of three state-of-the-art spatial filtering methods: common spatial patterns (CSPs) analysis, which requires binary label information; source power co-modulation (SPoC) analysis, which uses the subjects’ error rate as a target function; and canonical SPoC (cSPoC) analysis, which solely makes use of cross-frequency power correlations induced by different states of workload and thus represents an unsupervised approach. Finally, we investigated the effects of fusing brain signals and peripheral physiological measures (PPMs) and examined the added value for improving classification performance. Main results. Mean classification accuracies of 94%, 92% and 82% were achieved with CSP, SPoC, cSPoC, respectively. These methods outperformed the approaches that did not use spatial filtering and they extracted physiologically plausible components. The performance of the unsupervised cSPoC is significantly increased by augmenting it with PPM features. Significance. Our analyses ensured that the signal sources used for classification were of cortical origin and not contaminated with artifacts. Our findings show that workload states can be successfully differentiated from brain signals, even when less and less information from the experimental paradigm is used, thus paving the way for real-world applications in which label information may be noisy or entirely unavailable.

  16. Classification of grass pollen through the quantitative analysis of surface ornamentation and texture.

    PubMed

    Mander, Luke; Li, Mao; Mio, Washington; Fowlkes, Charless C; Punyasena, Surangi W

    2013-11-01

    Taxonomic identification of pollen and spores uses inherently qualitative descriptions of morphology. Consequently, identifications are restricted to categories that can be reliably classified by multiple analysts, resulting in the coarse taxonomic resolution of the pollen and spore record. Grass pollen represents an archetypal example; it is not routinely identified below family level. To address this issue, we developed quantitative morphometric methods to characterize surface ornamentation and classify grass pollen grains. This produces a means of quantifying morphological features that are traditionally described qualitatively. We used scanning electron microscopy to image 240 specimens of pollen from 12 species within the grass family (Poaceae). We classified these species by developing algorithmic features that quantify the size and density of sculptural elements on the pollen surface, and measure the complexity of the ornamentation they form. These features yielded a classification accuracy of 77.5%. In comparison, a texture descriptor based on modelling the statistical distribution of brightness values in image patches yielded a classification accuracy of 85.8%, and seven human subjects achieved accuracies between 68.33 and 81.67%. The algorithmic features we developed directly relate to biologically meaningful features of grass pollen morphology, and could facilitate direct interpretation of unsupervised classification results from fossil material.

  17. Application of Linear Discriminant Analysis in Dimensionality Reduction for Hand Motion Classification

    NASA Astrophysics Data System (ADS)

    Phinyomark, A.; Hu, H.; Phukpattaranont, P.; Limsakul, C.

    2012-01-01

    The classification of upper-limb movements based on surface electromyography (EMG) signals is an important issue in the control of assistive devices and rehabilitation systems. Increasing the number of EMG channels and features in order to increase the number of control commands can yield a high dimensional feature vector. To cope with the accuracy and computation problems associated with high dimensionality, it is commonplace to apply a processing step that transforms the data to a space of significantly lower dimensions with only a limited loss of useful information. Linear discriminant analysis (LDA) has been successfully applied as an EMG feature projection method. Recently, a number of extended LDA-based algorithms have been proposed, which are more competitive in terms of both classification accuracy and computational costs/times with classical LDA. This paper presents the findings of a comparative study of classical LDA and five extended LDA methods. From a quantitative comparison based on seven multi-feature sets, three extended LDA-based algorithms, consisting of uncorrelated LDA, orthogonal LDA and orthogonal fuzzy neighborhood discriminant analysis, produce better class separability when compared with a baseline system (without feature projection), principle component analysis (PCA), and classical LDA. Based on a 7-dimension time domain and time-scale feature vectors, these methods achieved respectively 95.2% and 93.2% classification accuracy by using a linear discriminant classifier.

  18. Overlay accuracy fundamentals

    NASA Astrophysics Data System (ADS)

    Kandel, Daniel; Levinski, Vladimir; Sapiens, Noam; Cohen, Guy; Amit, Eran; Klein, Dana; Vakshtein, Irina

    2012-03-01

    Currently, the performance of overlay metrology is evaluated mainly based on random error contributions such as precision and TIS variability. With the expected shrinkage of the overlay metrology budget to < 0.5nm, it becomes crucial to include also systematic error contributions which affect the accuracy of the metrology. Here we discuss fundamental aspects of overlay accuracy and a methodology to improve accuracy significantly. We identify overlay mark imperfections and their interaction with the metrology technology, as the main source of overlay inaccuracy. The most important type of mark imperfection is mark asymmetry. Overlay mark asymmetry leads to a geometrical ambiguity in the definition of overlay, which can be ~1nm or less. It is shown theoretically and in simulations that the metrology may enhance the effect of overlay mark asymmetry significantly and lead to metrology inaccuracy ~10nm, much larger than the geometrical ambiguity. The analysis is carried out for two different overlay metrology technologies: Imaging overlay and DBO (1st order diffraction based overlay). It is demonstrated that the sensitivity of DBO to overlay mark asymmetry is larger than the sensitivity of imaging overlay. Finally, we show that a recently developed measurement quality metric serves as a valuable tool for improving overlay metrology accuracy. Simulation results demonstrate that the accuracy of imaging overlay can be improved significantly by recipe setup optimized using the quality metric. We conclude that imaging overlay metrology, complemented by appropriate use of measurement quality metric, results in optimal overlay accuracy.

  19. Wavelet features in motion data classification

    NASA Astrophysics Data System (ADS)

    Szczesna, Agnieszka; Świtoński, Adam; Słupik, Janusz; Josiński, Henryk; Wojciechowski, Konrad

    2016-06-01

    The paper deals with the problem of motion data classification based on result of multiresolution analysis implemented in form of quaternion lifting scheme. Scheme processes directly on time series of rotations coded in form of unit quaternion signal. In the work new features derived from wavelet energy and entropy are proposed. To validate the approach gait database containing data of 30 different humans is used. The obtained results are satisfactory. The classification has over than 91% accuracy.

  20. 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.

  1. Accuracy and consistency of modern elastomeric pumps.

    PubMed

    Weisman, Robyn S; Missair, Andres; Pham, Phung; Gutierrez, Juan F; Gebhard, Ralf E

    2014-01-01

    Continuous peripheral nerve blockade has become a popular method of achieving postoperative analgesia for many surgical procedures. The safety and reliability of infusion pumps are dependent on their flow rate accuracy and consistency. Knowledge of pump rate profiles can help physicians determine which infusion pump is best suited for their clinical applications and specific patient population. Several studies have investigated the accuracy of portable infusion pumps. Using methodology similar to that used by Ilfeld et al, we investigated the accuracy and consistency of several current elastomeric pumps. PMID:25140510

  2. Harmonization of description and classification of fetal observations: achievements and problems still unresolved: report of the 7th Workshop on the Terminology in Developmental Toxicology Berlin, 4-6 May 2011.

    PubMed

    Solecki, Roland; Barbellion, Stephane; Bergmann, Brigitte; Bürgin, Heinrich; Buschmann, Jochen; Clark, Ruth; Comotto, Laura; Fuchs, Antje; Faqi, Ali Said; Gerspach, Ralph; Grote, Konstanze; Hakansson, Helen; Heinrich, Verena; Heinrich-Hirsch, Barbara; Hofmann, Thomas; Hübel, Ulrich; Inazaki, Thelma Helena; Khalil, Samia; Knudsen, Thomas B; Kudicke, Sabine; Lingk, Wolfgang; Makris, Susan; Müller, Simone; Paumgartten, Francisco; Pfeil, Rudolf; Rama, Elkiane Macedo; Schneider, Steffen; Shiota, Kohei; Tamborini, Eva; Tegelenbosch, Mariska; Ulbrich, Beate; van Duijnhoven, E A J; Wise, David; Chahoud, Ibrahim

    2013-01-01

    This article summarizes the 7th Workshop on the Terminology in Developmental Toxicology held in Berlin, May 4-6, 2011. The series of Berlin Workshops has been mainly concerned with the harmonization of terminology and classification of fetal anomalies in developmental toxicity studies. The main topics of the 7th Workshop were knowledge on the fate of anomalies after birth, use of Version 2 terminology for maternal-fetal observations and non-routinely used species, reclassification of "grey zone" anomalies and categorization of fetal observations for human health risk assessment. The paucity of data on health consequences of the postnatal permanence of fetal anomalies is relevant and further studies are needed. The Version 2 terminology is an important step forward and the terms listed in this glossary are considered also to be appropriate for most observations in non-routinely used species. Continuation of the Berlin Workshops was recommended. Topics suggested for the next Workshop were grouping of fetal observations for reporting and statistical analysis. PMID:22781580

  3. Regularization in Retrieval-Driven Classification of Clustered Microcalcifications for Breast Cancer

    PubMed Central

    Jing, Hao; Yang, Yongyi; Nishikawa, Robert M.

    2012-01-01

    We propose a regularization based approach for case-adaptive classification in computer-aided diagnosis (CAD) of breast cancer. The goal is to improve the classification accuracy on a query case by making use of a set of similar cases retrieved from an existing library of known cases. In the proposed approach, a prior is first derived from a traditional CAD classifier (which is typically pre-trained offline on a set of training cases). It is then used together with the retrieved similar cases to obtain an adaptive classifier on the query case. We consider two different forms for the regularization prior: one is fixed for all query cases and the other is allowed to vary with different query cases. In the experiments the proposed approach is demonstrated on a dataset of 1,006 clinical cases. The results show that it could achieve significant improvement in numerical efficiency compared with a previously proposed case adaptive approach (by about an order of magnitude) while maintaining similar (or better) improvement in classification accuracy; it could also adapt faster in performance with a small number of retrieved cases. Measured by the area of under the ROC curve (AUC), the regularization based approach achieved AUC = 0.8215, compared with AUC = 0.7329 for the baseline classifier (P-value = 0.001). PMID:22919363

  4. Computer-aided classification for remote sensing in agriculture and forestry in Northern Italy

    NASA Technical Reports Server (NTRS)

    Dejace, J.; Megier, J.; Mehl, W.

    1977-01-01

    A set of results concerning the processing and analysis of data from LANDSAT satellite and airborne scanner is presented. The possibility of performing inventories of irrigated crops-rice, planted groves-poplars, and natural forests in the mountians-beeches and chestnuts, is investigated in the Po valley and in an alphine site of Northern Italy. Accuracies around 95% or better, 70% and 60% respectively are achieved by using LANDSAT data and supervised classification. Discrimination of rice varieties is proved with 8 channels data from airborne scanner, processed after correction of the atmospheric effect due to the scanning angle, with and without linear feature selection of the data. The accuracies achieved range from 65% to more than 80%. The best results are obtained with the maximum likelihood classifier for normal parameters but rather close results are derived by using a modified version of the weighted euclidian distance between points, with consequent decrease in computing time around a factor 3.

  5. Testing of Land Cover Classification from Multispectral Airborne Laser Scanning Data

    NASA Astrophysics Data System (ADS)

    Bakuła, K.; Kupidura, P.; Jełowicki, Ł.

    2016-06-01

    Multispectral Airborne Laser Scanning provides a new opportunity for airborne data collection. It provides high-density topographic surveying and is also a useful tool for land cover mapping. Use of a minimum of three intensity images from a multiwavelength laser scanner and 3D information included in the digital surface model has the potential for land cover/use classification and a discussion about the application of this type of data in land cover/use mapping has recently begun. In the test study, three laser reflectance intensity images (orthogonalized point cloud) acquired in green, near-infrared and short-wave infrared bands, together with a digital surface model, were used in land cover/use classification where six classes were distinguished: water, sand and gravel, concrete and asphalt, low vegetation, trees and buildings. In the tested methods, different approaches for classification were applied: spectral (based only on laser reflectance intensity images), spectral with elevation data as additional input data, and spectro-textural, using morphological granulometry as a method of texture analysis of both types of data: spectral images and the digital surface model. The method of generating the intensity raster was also tested in the experiment. Reference data were created based on visual interpretation of ALS data and traditional optical aerial and satellite images. The results have shown that multispectral ALS data are unlike typical multispectral optical images, and they have a major potential for land cover/use classification. An overall accuracy of classification over 90% was achieved. The fusion of multi-wavelength laser intensity images and elevation data, with the additional use of textural information derived from granulometric analysis of images, helped to improve the accuracy of classification significantly. The method of interpolation for the intensity raster was not very helpful, and using intensity rasters with both first and last return

  6. Ground-level spectroscopy analyses and classification of coral reefs using a hyperspectral camera

    NASA Astrophysics Data System (ADS)

    Caras, T.; Karnieli, A.

    2013-09-01

    With the general aim of classification and mapping of coral reefs, remote sensing has traditionally been more difficult to implement in comparison with terrestrial equivalents. Images used for the marine environment suffer from environmental limitation (water absorption, scattering, and glint); sensor-related limitations (spectral and spatial resolution); and habitat limitation (substrate spectral similarity). Presented here is an advanced approach for ground-level surveying of a coral reef using a hyperspectral camera (400-1,000 nm) that is able to address all of these limitations. Used from the surface, the image includes a white reference plate that offers a solution for correcting the water column effect. The imaging system produces millimeter size pixels and 80 relevant bands. The data collected have the advantages of both a field point spectrometer (hyperspectral resolution) and a digital camera (spatial resolution). Finally, the availability of pure pixel imagery significantly improves the potential for substrate recognition in comparison with traditionally used remote sensing mixed pixels. In this study, an image of a coral reef table in the Gulf of Aqaba, Red Sea, was classified, demonstrating the benefits of this technology for the first time. Preprocessing includes testing of two normalization approaches, three spectral resolutions, and two spectral ranges. Trained classification was performed using support vector machine that was manually trained and tested against a digital image that provided empirical verification. For the classification of 5 core classes, the best results were achieved using a combination of a 450-660 nm spectral range, 5 nm wide bands, and the employment of red-band normalization. Overall classification accuracy was improved from 86 % for the original image to 99 % for the normalized image. Spectral resolution and spectral ranges seemed to have a limited effect on the classification accuracy. The proposed methodology and the use of

  7. Deep Learning in Label-free Cell Classification

    NASA Astrophysics Data System (ADS)

    Chen, Claire Lifan; Mahjoubfar, Ata; Tai, Li-Chia; Blaby, Ian K.; Huang, Allen; Niazi, Kayvan Reza; Jalali, Bahram

    2016-03-01

    Label-free cell analysis is essential to personalized genomics, cancer diagnostics, and drug development as it avoids adverse effects of staining reagents on cellular viability and cell signaling. However, currently available label-free cell assays mostly rely only on a single feature and lack sufficient differentiation. Also, the sample size analyzed by these assays is limited due to their low throughput. Here, we integrate feature extraction and deep learning with high-throughput quantitative imaging enabled by photonic time stretch, achieving record high accuracy in label-free cell classification. Our system captures quantitative optical phase and intensity images and extracts multiple biophysical features of individual cells. These biophysical measurements form a hyperdimensional feature space in which supervised learning is performed for cell classification. We compare various learning algorithms including artificial neural network, support vector machine, logistic regression, and a novel deep learning pipeline, which adopts global optimization of receiver operating characteristics. As a validation of the enhanced sensitivity and specificity of our system, we show classification of white blood T-cells against colon cancer cells, as well as lipid accumulating algal strains for biofuel production. This system opens up a new path to data-driven phenotypic diagnosis and better understanding of the heterogeneous gene expressions in cells.

  8. Multiple instance learning for classification of dementia in brain MRI.

    PubMed

    Tong, Tong; Wolz, Robin; Gao, Qinquan; Hajnal, Joseph V; Rueckert, Daniel

    2013-01-01

    Machine learning techniques have been widely used to support the diagnosis of neurological diseases such as dementia. Recent approaches utilize local intensity patterns within patches to derive voxelwise grading measures of disease. However, the relationships among these patches are usually ignored. In addition, there is some ambiguity in assigning disease labels to the extracted patches. Not all of the patches extracted from patients with dementia are characteristic of morphology associated with disease. In this paper, we propose to use a multiple instance learning method to address the problem of assigning training labels to the patches. In addition, a graph is built for each image to exploit the relationships among these patches, which aids the classification work. We illustrate the proposed approach in an application for the detection of Alzheimer's disease (AD): Using the baseline MR images of 834 subjects from the ADNI study, the proposed method can achieve a classification accuracy of 88.8% between AD patients and healthy controls, and 69.6% between patients with stable Mild Cognitive Impairment (MCI) and progressive MCI. These results compare favourably with state-of-the-art classification methods. PMID:24579190

  9. Deep Learning in Label-free Cell Classification.

    PubMed

    Chen, Claire Lifan; Mahjoubfar, Ata; Tai, Li-Chia; Blaby, Ian K; Huang, Allen; Niazi, Kayvan Reza; Jalali, Bahram

    2016-01-01

    Label-free cell analysis is essential to personalized genomics, cancer diagnostics, and drug development as it avoids adverse effects of staining reagents on cellular viability and cell signaling. However, currently available label-free cell assays mostly rely only on a single feature and lack sufficient differentiation. Also, the sample size analyzed by these assays is limited due to their low throughput. Here, we integrate feature extraction and deep learning with high-throughput quantitative imaging enabled by photonic time stretch, achieving record high accuracy in label-free cell classification. Our system captures quantitative optical phase and intensity images and extracts multiple biophysical features of individual cells. These biophysical measurements form a hyperdimensional feature space in which supervised learning is performed for cell classification. We compare various learning algorithms including artificial neural network, support vector machine, logistic regression, and a novel deep learning pipeline, which adopts global optimization of receiver operating characteristics. As a validation of the enhanced sensitivity and specificity of our system, we show classification of white blood T-cells against colon cancer cells, as well as lipid accumulating algal strains for biofuel production. This system opens up a new path to data-driven phenotypic diagnosis and better understanding of the heterogeneous gene expressions in cells. PMID:26975219

  10. 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.

  11. Deep Learning in Label-free Cell Classification.

    PubMed

    Chen, Claire Lifan; Mahjoubfar, Ata; Tai, Li-Chia; Blaby, Ian K; Huang, Allen; Niazi, Kayvan Reza; Jalali, Bahram

    2016-03-15

    Label-free cell analysis is essential to personalized genomics, cancer diagnostics, and drug development as it avoids adverse effects of staining reagents on cellular viability and cell signaling. However, currently available label-free cell assays mostly rely only on a single feature and lack sufficient differentiation. Also, the sample size analyzed by these assays is limited due to their low throughput. Here, we integrate feature extraction and deep learning with high-throughput quantitative imaging enabled by photonic time stretch, achieving record high accuracy in label-free cell classification. Our system captures quantitative optical phase and intensity images and extracts multiple biophysical features of individual cells. These biophysical measurements form a hyperdimensional feature space in which supervised learning is performed for cell classification. We compare various learning algorithms including artificial neural network, support vector machine, logistic regression, and a novel deep learning pipeline, which adopts global optimization of receiver operating characteristics. As a validation of the enhanced sensitivity and specificity of our system, we show classification of white blood T-cells against colon cancer cells, as well as lipid accumulating algal strains for biofuel production. This system opens up a new path to data-driven phenotypic diagnosis and better understanding of the heterogeneous gene expressions in cells.

  12. Deep Learning in Label-free Cell Classification

    PubMed Central

    Chen, Claire Lifan; Mahjoubfar, Ata; Tai, Li-Chia; Blaby, Ian K.; Huang, Allen; Niazi, Kayvan Reza; Jalali, Bahram

    2016-01-01

    Label-free cell analysis is essential to personalized genomics, cancer diagnostics, and drug development as it avoids adverse effects of staining reagents on cellular viability and cell signaling. However, currently available label-free cell assays mostly rely only on a single feature and lack sufficient differentiation. Also, the sample size analyzed by these assays is limited due to their low throughput. Here, we integrate feature extraction and deep learning with high-throughput quantitative imaging enabled by photonic time stretch, achieving record high accuracy in label-free cell classification. Our system captures quantitative optical phase and intensity images and extracts multiple biophysical features of individual cells. These biophysical measurements form a hyperdimensional feature space in which supervised learning is performed for cell classification. We compare various learning algorithms including artificial neural network, support vector machine, logistic regression, and a novel deep learning pipeline, which adopts global optimization of receiver operating characteristics. As a validation of the enhanced sensitivity and specificity of our system, we show classification of white blood T-cells against colon cancer cells, as well as lipid accumulating algal strains for biofuel production. This system opens up a new path to data-driven phenotypic diagnosis and better understanding of the heterogeneous gene expressions in cells. PMID:26975219

  13. Deep Learning in Label-free Cell Classification

    DOE PAGES

    Chen, Claire Lifan; Mahjoubfar, Ata; Tai, Li-Chia; Blaby, Ian K.; Huang, Allen; Niazi, Kayvan Reza; Jalali, Bahram

    2016-03-15

    Label-free cell analysis is essential to personalized genomics, cancer diagnostics, and drug development as it avoids adverse effects of staining reagents on cellular viability and cell signaling. However, currently available label-free cell assays mostly rely only on a single feature and lack sufficient differentiation. Also, the sample size analyzed by these assays is limited due to their low throughput. Here, we integrate feature extraction and deep learning with high-throughput quantitative imaging enabled by photonic time stretch, achieving record high accuracy in label-free cell classification. Our system captures quantitative optical phase and intensity images and extracts multiple biophysical features of individualmore » cells. These biophysical measurements form a hyperdimensional feature space in which supervised learning is performed for cell classification. We compare various learning algorithms including artificial neural network, support vector machine, logistic regression, and a novel deep learning pipeline, which adopts global optimization of receiver operating characteristics. As a validation of the enhanced sensitivity and specificity of our system, we show classification of white blood T-cells against colon cancer cells, as well as lipid accumulating algal strains for biofuel production. In conclusion, this system opens up a new path to data-driven phenotypic diagnosis and better understanding of the heterogeneous gene expressions in cells.« less

  14. Consensus of classification trees for skin sensitisation hazard prediction.

    PubMed

    Asturiol, D; Casati, S; Worth, A

    2016-10-01

    Since March 2013, it is no longer possible to market in the European Union (EU) cosmetics containing new ingredients tested on animals. Although several in silico alternatives are available and achievements have been made in the development and regulatory adoption of skin sensitisation non-animal tests, there is not yet a generally accepted approach for skin sensitisation assessment that would fully substitute the need for animal testing. The aim of this work was to build a defined approach (i.e. a predictive model based on readouts from various information sources that uses a fixed procedure for generating a prediction) for skin sensitisation hazard prediction (sensitiser/non-sensitiser) using Local Lymph Node Assay (LLNA) results as reference classifications. To derive the model, we built a dataset with high quality data from in chemico (DPRA) and in vitro (KeratinoSens™ and h-CLAT) methods, and it was complemented with predictions from several software packages. The modelling exercise showed that skin sensitisation hazard was better predicted by classification trees based on in silico predictions. The defined approach consists of a consensus of two classification trees that are based on descriptors that account for protein reactivity and structural features. The model showed an accuracy of 0.93, sensitivity of 0.98, and specificity of 0.85 for 269 chemicals. In addition, the defined approach provides a measure of confidence associated to the prediction. PMID:27458072

  15. Consensus of classification trees for skin sensitisation hazard prediction.

    PubMed

    Asturiol, D; Casati, S; Worth, A

    2016-10-01

    Since March 2013, it is no longer possible to market in the European Union (EU) cosmetics containing new ingredients tested on animals. Although several in silico alternatives are available and achievements have been made in the development and regulatory adoption of skin sensitisation non-animal tests, there is not yet a generally accepted approach for skin sensitisation assessment that would fully substitute the need for animal testing. The aim of this work was to build a defined approach (i.e. a predictive model based on readouts from various information sources that uses a fixed procedure for generating a prediction) for skin sensitisation hazard prediction (sensitiser/non-sensitiser) using Local Lymph Node Assay (LLNA) results as reference classifications. To derive the model, we built a dataset with high quality data from in chemico (DPRA) and in vitro (KeratinoSens™ and h-CLAT) methods, and it was complemented with predictions from several software packages. The modelling exercise showed that skin sensitisation hazard was better predicted by classification trees based on in silico predictions. The defined approach consists of a consensus of two classification trees that are based on descriptors that account for protein reactivity and structural features. The model showed an accuracy of 0.93, sensitivity of 0.98, and specificity of 0.85 for 269 chemicals. In addition, the defined approach provides a measure of confidence associated to the prediction.

  16. A Note on Document Classification with Small Training Data

    NASA Astrophysics Data System (ADS)

    Maeda, Yasunari; Yoshida, Hideki; Suzuki, Masakiyo; Matsushima, Toshiyasu

    Document classification is one of important topics in the field of NLP (Natural Language Processing). In the previous research a document classification method has been proposed which minimizes an error rate with reference to a Bayes criterion. But when the number of documents in training data is small, the accuracy of the previous method is low. So in this research we use estimating data in order to estimate prior distributions. When the training data is small the accuracy using estimating data is higher than the accuracy of the previous method. But when the training data is big the accuracy using estimating data is lower than the accuracy of the previous method. So in this research we also propose another technique whose accuracy is higher than the accuracy of the previous method when the training data is small, and is almost the same as the accuracy of the previous method when the training data is big.

  17. The decision tree approach to classification

    NASA Technical Reports Server (NTRS)

    Wu, C.; Landgrebe, D. A.; Swain, P. H.

    1975-01-01

    A class of multistage decision tree classifiers is proposed and studied relative to the classification of multispectral remotely sensed data. The decision tree classifiers are shown to have the potential for improving both the classification accuracy and the computation efficiency. Dimensionality in pattern recognition is discussed and two theorems on the lower bound of logic computation for multiclass classification are derived. The automatic or optimization approach is emphasized. Experimental results on real data are reported, which clearly demonstrate the usefulness of decision tree classifiers.

  18. Interpretable exemplar-based shape classification using constrained sparse linear models

    NASA Astrophysics Data System (ADS)

    Sigurdsson, Gunnar A.; Yang, Zhen; Tran, Trac D.; Prince, Jerry L.

    2015-03-01

    Many types of diseases manifest themselves as observable changes in the shape of the affected organs. Using shape classification, we can look for signs of disease and discover relationships between diseases. We formulate the problem of shape classification in a holistic framework that utilizes a lossless scalar field representation and a non-parametric classification based on sparse recovery. This framework generalizes over certain classes of unseen shapes while using the full information of the shape, bypassing feature extraction. The output of the method is the class whose combination of exemplars most closely approximates the shape, and furthermore, the algorithm returns the most similar exemplars along with their similarity to the shape, which makes the result simple to interpret. Our results show that the method offers accurate classification between three cerebellar diseases and controls in a database of cerebellar ataxia patients. For reproducible comparison, promising results are presented on publicly available 2D datasets, including the ETH-80 dataset where the method achieves 88.4% classification accuracy.

  19. A novel multi-manifold classification model via path-based clustering for image retrieval

    NASA Astrophysics Data System (ADS)

    Zhu, Rong; Yuan, Zhijun; Xuan, Junying

    2011-12-01

    Nowadays, with digital cameras and mass storage devices becoming increasingly affordable, each day thousands of pictures are taken and images on the Internet are emerged at an astonishing rate. Image retrieval is a process of searching valuable information that user demanded from huge images. However, it is hard to find satisfied results due to the well known "semantic gap". Image classification plays an essential role in retrieval process. But traditional methods will encounter problems when dealing with high-dimensional and large-scale image sets in applications. Here, we propose a novel multi-manifold classification model for image retrieval. Firstly, we simplify the classification of images from high-dimensional space into the one on low-dimensional manifolds, largely reducing the complexity of classification process. Secondly, considering that traditional distance measures often fail to find correct visual semantics of manifolds, especially when dealing with the images having complex data distribution, we also define two new distance measures based on path-based clustering, and further applied to the construction of a multi-class image manifold. One experiment was conducted on 2890 Web images. The comparison results between three methods show that the proposed method achieves the highest classification accuracy.

  20. Neighborhood Rough Set Reduction-Based Gene Selection and Prioritization for Gene Expression Profile Analysis and Molecular Cancer Classification

    PubMed Central

    Hou, Mei-Ling; Wang, Shu-Lin; Li, Xue-Ling; Lei, Ying-Ke

    2010-01-01

    Selection of reliable cancer biomarkers is crucial for gene expression profile-based precise diagnosis of cancer type and successful treatment. However, current studies are confronted with overfitting and dimensionality curse in tumor classification and false positives in the identification of cancer biomarkers. Here, we developed a novel gene-ranking method based on neighborhood rough set reduction for molecular cancer classification based on gene expression profile. Comparison with other methods such as PAM, ClaNC, Kruskal-Wallis rank sum test, and Relief-F, our method shows that only few top-ranked genes could achieve higher tumor classification accuracy. Moreover, although the selected genes are not typical of known oncogenes, they are found to play a crucial role in the occurrence of tumor through searching the scientific literature and analyzing protein interaction partners, which may be used as candidate cancer biomarkers. PMID:20625410

  1. Soft Classification of Diffractive Interactions at the LHC

    SciTech Connect

    Kuusela, Mikael; Malmi, Eric; Orava, Risto; Vatanen, Tommi

    2011-07-15

    Multivariate machine learning techniques provide an alternative to the rapidity gap method for event-by-event identification and classification of diffraction in hadron-hadron collisions. Traditionally, such methods assign each event exclusively to a single class producing classification errors in overlap regions of data space. As an alternative to this so called hard classification approach, we propose estimating posterior probabilities of each diffractive class and using these estimates to weigh event contributions to physical observables. It is shown with a Monte Carlo study that such a soft classification scheme is able to reproduce observables such as multiplicity distributions and relative event rates with a much higher accuracy than hard classification.

  2. Remote Sensing Information Classification

    NASA Technical Reports Server (NTRS)

    Rickman, Douglas L.

    2008-01-01

    This viewgraph presentation reviews the classification of Remote Sensing data in relation to epidemiology. Classification is a way to reduce the dimensionality and precision to something a human can understand. Classification changes SCALAR data into NOMINAL data.

  3. Classification and knowledge

    NASA Technical Reports Server (NTRS)

    Kurtz, Michael J.

    1989-01-01

    Automated procedures to classify objects are discussed. The classification problem is reviewed, and the relation of epistemology and classification is considered. The classification of stellar spectra and of resolved images of galaxies is addressed.

  4. Automatic retinal vessel classification using a Least Square-Support Vector Machine in VAMPIRE.

    PubMed

    Relan, D; MacGillivray, T; Ballerini, L; Trucco, E

    2014-01-01

    It is important to classify retinal blood vessels into arterioles and venules for computerised analysis of the vasculature and to aid discovery of disease biomarkers. For instance, zone B is the standardised region of a retinal image utilised for the measurement of the arteriole to venule width ratio (AVR), a parameter indicative of microvascular health and systemic disease. We introduce a Least Square-Support Vector Machine (LS-SVM) classifier for the first time (to the best of our knowledge) to label automatically arterioles and venules. We use only 4 image features and consider vessels inside zone B (802 vessels from 70 fundus camera images) and in an extended zone (1,207 vessels, 70 fundus camera images). We achieve an accuracy of 94.88% and 93.96% in zone B and the extended zone, respectively, with a training set of 10 images and a testing set of 60 images. With a smaller training set of only 5 images and the same testing set we achieve an accuracy of 94.16% and 93.95%, respectively. This experiment was repeated five times by randomly choosing 10 and 5 images for the training set. Mean classification accuracy are close to the above mentioned result. We conclude that the performance of our system is very promising and outperforms most recently reported systems. Our approach requires smaller training data sets compared to others but still results in a similar or higher classification rate.

  5. Feature extraction and classification for EEG signals using wavelet transform and machine learning techniques.

    PubMed

    Amin, Hafeez Ullah; Malik, Aamir Saeed; Ahmad, Rana Fayyaz; Badruddin, Nasreen; Kamel, Nidal; Hussain, Muhammad; Chooi, Weng-Tink

    2015-03-01

    This paper describes a discrete wavelet transform-based feature extraction scheme for the classification of EEG signals. In this scheme, the discrete wavelet transform is applied on EEG signals and the relative wavelet energy is calculated in terms of detailed coefficients and the approximation coefficients of the last decomposition level. The extracted relative wavelet energy features are passed to classifiers for the classification purpose. The EEG dataset employed for the validation of the proposed method consisted of two classes: (1) the EEG signals recorded during the complex cognitive task--Raven's advance progressive metric test and (2) the EEG signals recorded in rest condition--eyes open. The performance of four different classifiers was evaluated with four performance measures, i.e., accuracy, sensitivity, specificity and precision values. The accuracy was achieved above 98 % by the support vector machine, multi-layer perceptron and the K-nearest neighbor classifiers with approximation (A4) and detailed coefficients (D4), which represent the frequency range of 0.53-3.06 and 3.06-6.12 Hz, respectively. The findings of this study demonstrated that the proposed feature extraction approach has the potential to classify the EEG signals recorded during a complex cognitive task by achieving a high accuracy rate.

  6. Study of wavelet packet energy entropy for emotion classification in speech and glottal signals

    NASA Astrophysics Data System (ADS)

    He, Ling; Lech, Margaret; Zhang, Jing; Ren, Xiaomei; Deng, Lihua

    2013-07-01

    The automatic speech emotion recognition has important applications in human-machine communication. Majority of current research in this area is focused on finding optimal feature parameters. In recent studies, several glottal features were examined as potential cues for emotion differentiation. In this study, a new type of feature parameter is proposed, which calculates energy entropy on values within selected Wavelet Packet frequency bands. The modeling and classification tasks are conducted using the classical GMM algorithm. The experiments use two data sets: the Speech Under Simulated Emotion (SUSE) data set annotated with three different emotions (angry, neutral and soft) and Berlin Emotional Speech (BES) database annotated with seven different emotions (angry, bored, disgust, fear, happy, sad and neutral). The average classification accuracy achieved for the SUSE data (74%-76%) is significantly higher than the accuracy achieved for the BES data (51%-54%). In both cases, the accuracy was significantly higher than the respective random guessing levels (33% for SUSE and 14.3% for BES).

  7. Land cover classification of VHR airborne images for citrus grove identification

    NASA Astrophysics Data System (ADS)

    Amorós López, J.; Izquierdo Verdiguier, E.; Gómez Chova, L.; Muñoz Marí, J.; Rodríguez Barreiro, J. Z.; Camps Valls, G.; Calpe Maravilla, J.

    Managing land resources using remote sensing techniques is becoming a common practice. However, data analysis procedures should satisfy the high accuracy levels demanded by users (public or private companies and governments) in order to be extensively used. This paper presents a multi-stage classification scheme to update the citrus Geographical Information System (GIS) of the Comunidad Valenciana region (Spain). Spain is the first citrus fruit producer in Europe and the fourth in the world. In particular, citrus fruits represent 67% of the agricultural production in this region, with a total production of 4.24 million tons (campaign 2006-2007). The citrus GIS inventory, created in 2001, needs to be regularly updated in order to monitor changes quickly enough, and allow appropriate policy making and citrus production forecasting. Automatic methods are proposed in this work to facilitate this update, whose processing scheme is summarized as follows. First, an object-oriented feature extraction process is carried out for each cadastral parcel from very high spatial resolution aerial images (0.5 m). Next, several automatic classifiers (decision trees, artificial neural networks, and support vector machines) are trained and combined to improve the final classification accuracy. Finally, the citrus GIS is automatically updated if a high enough level of confidence, based on the agreement between classifiers, is achieved. This is the case for 85% of the parcels and accuracy results exceed 94%. The remaining parcels are classified by expert photo-interpreters in order to guarantee the high accuracy demanded by policy makers.

  8. AVHRR channel selection for land cover classification

    USGS Publications Warehouse

    Maxwell, S.K.; Hoffer, R.M.; Chapman, P.L.

    2002-01-01

    Mapping land cover of large regions often requires processing of satellite images collected from several time periods at many spectral wavelength channels. However, manipulating and processing large amounts of image data increases the complexity and time, and hence the cost, that it takes to produce a land cover map. Very few studies have evaluated the importance of individual Advanced Very High Resolution Radiometer (AVHRR) channels for discriminating cover types, especially the thermal channels (channels 3, 4 and 5). Studies rarely perform a multi-year analysis to determine the impact of inter-annual variability on the classification results. We evaluated 5 years of AVHRR data using combinations of the original AVHRR spectral channels (1-5) to determine which channels are most important for cover type discrimination, yet stabilize inter-annual variability. Particular attention was placed on the channels in the thermal portion of the spectrum. Fourteen cover types over the entire state of Colorado were evaluated using a supervised classification approach on all two-, three-, four- and five-channel combinations for seven AVHRR biweekly composite datasets covering the entire growing season for each of 5 years. Results show that all three of the major portions of the electromagnetic spectrum represented by the AVHRR sensor are required to discriminate cover types effectively and stabilize inter-annual variability. Of the two-channel combinations, channels 1 (red visible) and 2 (near-infrared) had, by far, the highest average overall accuracy (72.2%), yet the inter-annual classification accuracies were highly variable. Including a thermal channel (channel 4) significantly increased the average overall classification accuracy by 5.5% and stabilized inter-annual variability. Each of the thermal channels gave similar classification accuracies; however, because of the problems in consistently interpreting channel 3 data, either channel 4 or 5 was found to be a more

  9. Real-Time Calibration for Rapid Raman Ordinary Chondrite Classification

    NASA Astrophysics Data System (ADS)

    Welzenbach, L. C.

    2016-08-01

    Raman spectroscopy is a classification tool being used by other researchers to classify chondrites from NWA and Antarctica. Neon reference spectrum of sufficient fidelity is taken concurrently to calibrate the spectrum to sub-cm-1 accuracy.

  10. Automatic approach to solve the morphological galaxy classification problem using the sparse representation technique and dictionary learning

    NASA Astrophysics Data System (ADS)

    Diaz-Hernandez, R.; Ortiz-Esquivel, A.; Peregrina-Barreto, H.; Altamirano-Robles, L.; Gonzalez-Bernal, J.

    2016-06-01

    The observation of celestial objects in the sky is a practice that helps astronomers to understand the way in which the Universe is structured. However, due to the large number of observed objects with modern telescopes, the analysis of these by hand is a difficult task. An important part in galaxy research is the morphological structure classification based on the Hubble sequence. In this research, we present an approach to solve the morphological galaxy classification problem in an automatic way by using the Sparse Representation technique and dictionary learning with K-SVD. For the tests in this work, we use a database of galaxies extracted from the Principal Galaxy Catalog (PGC) and the APM Equatorial Catalogue of Galaxies obtaining a total of 2403 useful galaxies. In order to represent each galaxy frame, we propose to calculate a set of 20 features such as Hu's invariant moments, galaxy nucleus eccentricity, gabor galaxy ratio and some other features commonly used in galaxy classification. A stage of feature relevance analysis was performed using Relief-f in order to determine which are the best parameters for the classification tests using 2, 3, 4, 5, 6 and 7 galaxy classes making signal vectors of different length values with the most important features. For the classification task, we use a 20-random cross-validation technique to evaluate classification accuracy with all signal sets achieving a score of 82.27 % for 2 galaxy classes and up to 44.27 % for 7 galaxy classes.

  11. Statistically invalid classification of high throughput gene expression data.

    PubMed

    Barbash, Shahar; Soreq, Hermona

    2013-01-01

    Classification analysis based on high throughput data is a common feature in neuroscience and other fields of science, with a rapidly increasing impact on both basic biology and disease-related studies. The outcome of such classifications often serves to delineate novel biochemical mechanisms in health and disease states, identify new targets for therapeutic interference, and develop innovative diagnostic approaches. Given the importance of this type of studies, we screened 111 recently-published high-impact manuscripts involving classification analysis of gene expression, and found that 58 of them (53%) based their conclusions on a statistically invalid method which can lead to bias in a statistical sense (lower true classification accuracy then the reported classification accuracy). In this report we characterize the potential methodological error and its scope, investigate how it is influenced by different experimental parameters, and describe statistically valid methods for avoiding such classification mistakes.

  12. Robust classification of DNA damage patterns in single cell gel electrophoresis.

    PubMed

    Lee, Taehoon; Lee, Sungmin; Sim, Woo Young; Jung, Yu Mi; Han, Sunmi; Chung, Chanil; Chang, Jay Junkeun; Min, Hyeyoung; Yoon, Sungroh

    2013-01-01

    Single cell gel electrophoresis, also known as comet assay, has been widely used for assessing the effect of genotoxicity and detecting DNA damage of individual eukaryotic cells. There exist established imaging techniques for cometassay analysis, but these platforms have limitations such as required user interventions, low throughput, and weakness to noise caused by incomplete dyeing of fluorescent materials and other experimental errors. To resolve these, we propose a novel procedure for analyzing comet assay images, which considers various DNA damage patterns and classifies them in a robust manner. We tested our approach with twenty golden data sets containing over 300 comets and achieved satisfactory classification accuracy. PMID:24110525

  13. Membrane positioning for high- and low-resolution protein structures through a binary classification approach.

    PubMed

    Postic, Guillaume; Ghouzam, Yassine; Guiraud, Vincent; Gelly, Jean-Christophe

    2016-03-01

    The critical importance of algorithms for orienting proteins in the lipid bilayer stems from the extreme difficulty in obtaining experimental data about the membrane boundaries. Here, we present a computational method for positioning protein structures in the membrane, based on the sole alpha carbon coordinates and, therefore, compatible with both high and low structural resolutions. Our algorithm follows a new and simple approach, by treating the membrane assignment problem as a binary classification. Compared with the state-of-the-art algorithms, our method achieves similar accuracy, while being faster. Finally, our open-source software is also capable of processing coarse-grained models of protein structures. PMID:26685702

  14. Texture-Based Automated Lithological Classification Using Aeromagenetic Anomaly Images

    USGS Publications Warehouse

    Shankar, Vivek

    2009-01-01

    This report consists of a thesis submitted to the faculty of the Department of Electrical and Computer Engineering, in partial fulfillment of the requirements for the degree of Master of Science, Graduate College, The University of Arizona, 2004 Aeromagnetic anomaly images are geophysical prospecting tools frequently used in the exploration of metalliferous minerals and hydrocarbons. The amplitude and texture content of these images provide a wealth of information to geophysicists who attempt to delineate the nature of the Earth's upper crust. These images prove to be extremely useful in remote areas and locations where the minerals of interest are concealed by basin fill. Typically, geophysicists compile a suite of aeromagnetic anomaly images, derived from amplitude and texture measurement operations, in order to obtain a qualitative interpretation of the lithological (rock) structure. Texture measures have proven to be especially capable of capturing the magnetic anomaly signature of unique lithological units. We performed a quantitative study to explore the possibility of using texture measures as input to a machine vision system in order to achieve automated classification of lithological units. This work demonstrated a significant improvement in classification accuracy over random guessing based on a priori probabilities. Additionally, a quantitative comparison between the performances of five classes of texture measures in their ability to discriminate lithological units was achieved.

  15. Automatic classification of DMSA scans using an artificial neural network.

    PubMed

    Wright, J W; Duguid, R; McKiddie, F; Staff, R T

    2014-04-01

    DMSA imaging is carried out in nuclear medicine to assess the level of functional renal tissue in patients. This study investigated the use of an artificial neural network to perform diagnostic classification of these scans. Using the radiological report as the gold standard, the network was trained to classify DMSA scans as positive or negative for defects using a representative sample of 257 previously reported images. The trained network was then independently tested using a further 193 scans and achieved a binary classification accuracy of 95.9%. The performance of the network was compared with three qualified expert observers who were asked to grade each scan in the 193 image testing set on a six point defect scale, from 'definitely normal' to 'definitely abnormal'. A receiver operating characteristic analysis comparison between a consensus operator, generated from the scores of the three expert observers, and the network revealed a statistically significant increase (α < 0.05) in performance between the network and operators. A further result from this work was that when suitably optimized, a negative predictive value of 100% for renal defects was achieved by the network, while still managing to identify 93% of the negative cases in the dataset. These results are encouraging for application of such a network as a screening tool or quality assurance assistant in clinical practice.

  16. Automatic classification of DMSA scans using an artificial neural network

    NASA Astrophysics Data System (ADS)

    Wright, J. W.; Duguid, R.; Mckiddie, F.; Staff, R. T.

    2014-04-01

    DMSA imaging is carried out in nuclear medicine to assess the level of functional renal tissue in patients. This study investigated the use of an artificial neural network to perform diagnostic classification of these scans. Using the radiological report as the gold standard, the network was trained to classify DMSA scans as positive or negative for defects using a representative sample of 257 previously reported images. The trained network was then independently tested using a further 193 scans and achieved a binary classification accuracy of 95.9%. The performance of the network was compared with three qualified expert observers who were asked to grade each scan in the 193 image testing set on a six point defect scale, from ‘definitely normal’ to ‘definitely abnormal’. A receiver operating characteristic analysis comparison between a consensus operator, generated from the scores of the three expert observers, and the network revealed a statistically significant increase (α < 0.05) in performance between the network and operators. A further result from this work was that when suitably optimized, a negative predictive value of 100% for renal defects was achieved by the network, while still managing to identify 93% of the negative cases in the dataset. These results are encouraging for application of such a network as a screening tool or quality assurance assistant in clinical practice.

  17. An adaptive unsupervised hyperspectral classification method based on Gaussian distribution

    NASA Astrophysics Data System (ADS)

    Yue, Jiang; Wu, Jing-wei; Zhang, Yi; Bai, Lian-fa

    2014-11-01

    In order to achieve adaptive unsupervised clustering in the high precision, a method using Gaussian distribution to fit the similarity of the inter-class and the noise distribution is proposed in this paper, and then the automatic segmentation threshold is determined by the fitting result. First, according with the similarity measure of the spectral curve, this method assumes that the target and the background both in Gaussian distribution, the distribution characteristics is obtained through fitting the similarity measure of minimum related windows and center pixels with Gaussian function, and then the adaptive threshold is achieved. Second, make use of the pixel minimum related windows to merge adjacent similar pixels into a picture-block, then the dimensionality reduction is completed and the non-supervised classification is realized. AVIRIS data and a set of hyperspectral data we caught are used to evaluate the performance of the proposed method. Experimental results show that the proposed algorithm not only realizes the adaptive but also outperforms K-MEANS and ISODATA on the classification accuracy, edge recognition and robustness.

  18. Critical thinking and accuracy of nurses' diagnoses.

    PubMed

    Lunney, Margaret

    2003-01-01

    Interpretations of patient data are complex and diverse, contributing to a risk of low accuracy nursing diagnoses. This risk is confirmed in research findings that accuracy of nurses' diagnoses varied widely from high to low. Highly accurate diagnoses are essential, however, to guide nursing interventions for the achievement of positive health outcomes. Development of critical thinking abilities is likely to improve accuracy of nurses' diagnoses. New views of critical thinking serve as a basis for critical thinking in nursing. Seven cognitive skills and ten habits of mind are identified as dimensions of critical thinking for use in the diagnostic process. Application of the cognitive skills of critical thinking illustrates the importance of using critical thinking for accuracy of nurses' diagnoses. Ten strategies are proposed for self-development of critical thinking abilities.

  19. Optimal design of robot accuracy compensators

    SciTech Connect

    Zhuang, H.; Roth, Z.S. . Robotics Center and Electrical Engineering Dept.); Hamano, Fumio . Dept. of Electrical Engineering)

    1993-12-01

    The problem of optimal design of robot accuracy compensators is addressed. Robot accuracy compensation requires that actual kinematic parameters of a robot be previously identified. Additive corrections of joint commands, including those at singular configurations, can be computed without solving the inverse kinematics problem for the actual robot. This is done by either the damped least-squares (DLS) algorithm or the linear quadratic regulator (LQR) algorithm, which is a recursive version of the DLS algorithm. The weight matrix in the performance index can be selected to achieve specific objectives, such as emphasizing end-effector's positioning accuracy over orientation accuracy or vice versa, or taking into account proximity to robot joint travel limits and singularity zones. The paper also compares the LQR and the DLS algorithms in terms of computational complexity, storage requirement, and programming convenience. Simulation results are provided to show the effectiveness of the algorithms.

  20. Classification of hyperspectral imagery for identifying fecal and ingesta contaminants

    NASA Astrophysics Data System (ADS)

    Park, Bosoon; Windham, William R.; Lawrence, Kurt C.; Smith, Douglas P.

    2004-03-01

    This paper presents the research results of the performance of classification methods for hyperspectral poultry imagery to identify fecal and ingesta contaminants on the surface of broiler carcasses. A pushbroom line-scan hyperspectral imager was used to acquire hyperspectral data with 512 narrow bands covered from 400 to 900 nm wavelengths. Three different feces from digestive tracts (duodenum, ceca, colon), and ingesta were used as contaminants. These contaminants were collected from the broiler carcasses fed by corn, milo, and wheat with soybean meals. For the selection of optimum classifier, various widely used supervised classification methods (parallelepiped, minimum distance, Mahalanobis distance, maximum likelihood, spectral angle mapper, and binary coding) were investigated. The classification accuracies ranged from 62.94% to 92.27%. The highest classification accuracy for identifying contaminants for corn fed carcasses was 92.27% with spectral angle mapper classifier. While, the classification accuracy was 82.02% with maximum likelihood method for milo fed carcasses and 91.16% accuracy was obtained for wheat fed carcasses when same classification method was used. The mean classification accuracy obtained in this study for classifying fecal and ingesta contaminants was 90.21%.

  1. Item Selection Criteria with Practical Constraints for Computerized Classification Testing

    ERIC Educational Resources Information Center

    Lin, Chuan-Ju

    2011-01-01

    This study compares four item selection criteria for a two-category computerized classification testing: (1) Fisher information (FI), (2) Kullback-Leibler information (KLI), (3) weighted log-odds ratio (WLOR), and (4) mutual information (MI), with respect to the efficiency and accuracy of classification decision using the sequential probability…

  2. Arizona Vegetation Resource Inventory (AVRI) accuracy assessment

    USGS Publications Warehouse

    Szajgin, John; Pettinger, L.R.; Linden, D.S.; Ohlen, D.O.

    1982-01-01

    A quantitative accuracy assessment was performed for the vegetation classification map produced as part of the Arizona Vegetation Resource Inventory (AVRI) project. This project was a cooperative effort between the Bureau of Land Management (BLM) and the Earth Resources Observation Systems (EROS) Data Center. The objective of the accuracy assessment was to estimate (with a precision of ?10 percent at the 90 percent confidence level) the comission error in each of the eight level II hierarchical vegetation cover types. A stratified two-phase (double) cluster sample was used. Phase I consisted of 160 photointerpreted plots representing clusters of Landsat pixels, and phase II consisted of ground data collection at 80 of the phase I cluster sites. Ground data were used to refine the phase I error estimates by means of a linear regression model. The classified image was stratified by assigning each 15-pixel cluster to the stratum corresponding to the dominant cover type within each cluster. This method is known as stratified plurality sampling. Overall error was estimated to be 36 percent with a standard error of 2 percent. Estimated error for individual vegetation classes ranged from a low of 10 percent ?6 percent for evergreen woodland to 81 percent ?7 percent for cropland and pasture. Total cost of the accuracy assessment was $106,950 for the one-million-hectare study area. The combination of the stratified plurality sampling (SPS) method of sample allocation with double sampling provided the desired estimates within the required precision levels. The overall accuracy results confirmed that highly accurate digital classification of vegetation is difficult to perform in semiarid environments, due largely to the sparse vegetation cover. Nevertheless, these techniques show promise for providing more accurate information than is presently available for many BLM-administered lands.

  3. Random forests for classification in ecology.

    PubMed

    Cutler, D Richard; Edwards, Thomas C; Beard, Karen H; Cutler, Adele; Hess, Kyle T; Gibson, Jacob; Lawler, Joshua J

    2007-11-01

    Classification procedures are some of the most widely used statistical methods in ecology. Random forests (RF) is a new and powerful statistical classifier that is well established in other disciplines but is relatively unknown in ecology. Advantages of RF compared to other statistical classifiers include (1) very high classification accuracy; (2) a novel method of determining variable importance; (3) ability to model complex interactions among predictor variables; (4) flexibility to perform several types of statistical data analysis, including regression, classification, survival analysis, and unsupervised learning; and (5) an algorithm for imputing missing values. We compared the accuracies of RF and four other commonly used statistical classifiers using data on invasive plant species presence in Lava Beds National Monument, California, USA, rare lichen species presence in the Pacific Northwest, USA, and nest sites for cavity nesting birds in the Uinta Mountains, Utah, USA. We observed high classification accuracy in all applications as measured by cross-validation and, in the case of the lichen data, by independent test data, when comparing RF to other common classification methods. We also observed that the variables that RF identified as most important for classifying invasive plant species coincided with expectations based on the literature.

  4. Random forests for classification in ecology

    USGS Publications Warehouse

    Cutler, D.R.; Edwards, T.C.; Beard, K.H.; Cutler, A.; Hess, K.T.; Gibson, J.; Lawler, J.J.

    2007-01-01

    Classification procedures are some of the most widely used statistical methods in ecology. Random forests (RF) is a new and powerful statistical classifier that is well established in other disciplines but is relatively unknown in ecology. Advantages of RF compared to other statistical classifiers include (1) very high classification accuracy; (2) a novel method of determining variable importance; (3) ability to model complex interactions among predictor variables; (4) flexibility to perform several types of statistical data analysis, including regression, classification, survival analysis, and unsupervised learning; and (5) an algorithm for imputing missing values. We compared the accuracies of RF and four other commonly used statistical classifiers using data on invasive plant species presence in Lava Beds National Monument, California, USA, rare lichen species presence in the Pacific Northwest, USA, and nest sites for cavity nesting birds in the Uinta Mountains, Utah, USA. We observed high classification accuracy in all applications as measured by cross-validation and, in the case of the lichen data, by independent test data, when comparing RF to other common classification methods. We also observed that the variables that RF identified as most important for classifying invasive plant species coincided with expectations based on the literature. ?? 2007 by the Ecological Society of America.

  5. Retinal vasculature classification using novel multifractal features

    NASA Astrophysics Data System (ADS)

    Ding, Y.; Ward, W. O. C.; Duan, Jinming; Auer, D. P.; Gowland, Penny; Bai, L.

    2015-11-01

    Retinal blood vessels have been implicated in a large number of diseases including diabetic retinopathy and cardiovascular diseases, which cause damages to retinal blood vessels. The availability of retinal vessel imaging provides an excellent opportunity for monitoring and diagnosis of retinal diseases, and automatic analysis of retinal vessels will help with the processes. However, state of the art vascular analysis methods such as counting the number of branches or measuring the curvature and diameter of individual vessels are unsuitable for the microvasculature. There has been published research using fractal analysis to calculate fractal dimensions of retinal blood vessels, but so far there has been no systematic research extracting discriminant features from retinal vessels for classifications. This paper introduces new methods for feature extraction from multifractal spectra of retinal vessels for classification. Two publicly available retinal vascular image databases are used for the experiments, and the proposed methods have produced accuracies of 85.5% and 77% for classification of healthy and diabetic retinal vasculatures. Experiments show that classification with multiple fractal features produces better rates compared with methods using a single fractal dimension value. In addition to this, experiments also show that classification accuracy can be affected by the accuracy of vessel segmentation algorithms.

  6. Potentials of RapidEye time series for improved classification of crop rotations in heterogeneous agricultural landscapes: experiences from irrigation systems in Central Asia

    NASA Astrophysics Data System (ADS)

    Conrad, Christopher; Machwitz, Miriam; Schorcht, Gunther; Löw, Fabian; Fritsch, Sebastian; Dech, Stefan

    2011-11-01

    In Central Asia, more than eight Million ha of agricultural land are under irrigation. But severe degradation problems and unreliable water distribution have caused declining yields during the past decades. Reliable and area-wide information about crops can be seen as important step to elaborate options for sustainable land and water management. Experiences from RapidEye classifications of crop in Central Asia are exemplarily shown during a classification of eight crop classes including three rotations with winter wheat, cotton, rice, and fallow land in the Khorezm region of Uzbekistan covering 230,000 ha of irrigated land. A random forest generated by using 1215 field samples was applied to multitemporal RapidEye data acquired during the vegetation period 2010. But RapidEye coverage varied and did not allow for generating temporally consistent mosaics covering the entire region. To classify all 55,188 agricultural parcels in the region three classification zones were classified separately. The zoning allowed for including at least three observation periods into classification. Overall accuracy exceeded 85 % for all classification zones. Highest accuracies of 87.4 % were achieved by including five spatiotemporal composites of RapidEye. Class-wise accuracy assessments showed the usefulness of selecting time steps which represent relevant phenological phases of the vegetation period. The presented approach can support regional crop inventory. Accurate classification results in early stages of the cropping season permit recalculation of crop water demands and reallocation of irrigation water. The high temporal and spatial resolution of RapidEye can be concluded highly beneficial for agricultural land use classifications in entire Central Asia.

  7. Accuracy of Pressure Sensitive Paint

    NASA Technical Reports Server (NTRS)

    Liu, Tianshu; Guille, M.; Sullivan, J. P.

    2001-01-01

    Uncertainty in pressure sensitive paint (PSP) measurement is investigated from a standpoint of system modeling. A functional relation between the imaging system output and luminescent emission from PSP is obtained based on studies of radiative energy transports in PSP and photodetector response to luminescence. This relation provides insights into physical origins of various elemental error sources and allows estimate of the total PSP measurement uncertainty contributed by the elemental errors. The elemental errors and their sensitivity coefficients in the error propagation equation are evaluated. Useful formulas are given for the minimum pressure uncertainty that PSP can possibly achieve and the upper bounds of the elemental errors to meet required pressure accuracy. An instructive example of a Joukowsky airfoil in subsonic flows is given to illustrate uncertainty estimates in PSP measurements.

  8. High-accuracy EUV reflectometer

    NASA Astrophysics Data System (ADS)

    Hinze, U.; Fokoua, M.; Chichkov, B.

    2007-03-01

    Developers and users of EUV-optics need precise tools for the characterization of their products. Often a measurement accuracy of 0.1% or better is desired to detect and study slow-acting aging effect or degradation by organic contaminants. To achieve a measurement accuracy of 0.1% an EUV-source is required which provides an excellent long-time stability, namely power stability, spatial stability and spectral stability. Naturally, it should be free of debris. An EUV-source particularly suitable for this task is an advanced electron-based EUV-tube. This EUV source provides an output of up to 300 μW at 13.5 nm. Reflectometers benefit from the excellent long-time stability of this tool. We design and set up different reflectometers using EUV-tubes for the precise characterisation of EUV-optics, such as debris samples, filters, multilayer mirrors, grazing incidence optics, collectors and masks. Reflectivity measurements from grazing incidence to near normal incidence as well as transmission studies were realised at a precision of down to 0.1%. The reflectometers are computer-controlled and allow varying and scanning all important parameters online. The concepts of a sample reflectometer is discussed and results are presented. The devices can be purchased from the Laser Zentrum Hannover e.V.

  9. Automatic classification of small bowel mucosa alterations in celiac disease for confocal laser endomicroscopy

    NASA Astrophysics Data System (ADS)

    Boschetto, Davide; Di Claudio, Gianluca; Mirzaei, Hadis; Leong, Rupert; Grisan, Enrico

    2016-03-01

    Celiac disease (CD) is an immune-mediated enteropathy triggered by exposure to gluten and similar proteins, affecting genetically susceptible persons, increasing their risk of different complications. Small bowels mucosa damage due to CD involves various degrees of endoscopically relevant lesions, which are not easily recognized: their overall sensitivity and positive predictive values are poor even when zoom-endoscopy is used. Confocal Laser Endomicroscopy (CLE) allows skilled and trained experts to qualitative evaluate mucosa alteration such as a decrease in goblet cells density, presence of villous atrophy or crypt hypertrophy. We present a method for automatically classifying CLE images into three different classes: normal regions, villous atrophy and crypt hypertrophy. This classification is performed after a features selection process, in which four features are extracted from each image, through the application of homomorphic filtering and border identification through Canny and Sobel operators. Three different classifiers have been tested on a dataset of 67 different images labeled by experts in three classes (normal, VA and CH): linear approach, Naive-Bayes quadratic approach and a standard quadratic analysis, all validated with a ten-fold cross validation. Linear classification achieves 82.09% accuracy (class accuracies: 90.32% for normal villi, 82.35% for VA and 68.42% for CH, sensitivity: 0.68, specificity 1.00), Naive Bayes analysis returns 83.58% accuracy (90.32% for normal villi, 70.59% for VA and 84.21% for CH, sensitivity: 0.84 specificity: 0.92), while the quadratic analysis achieves a final accuracy of 94.03% (96.77% accuracy for normal villi, 94.12% for VA and 89.47% for CH, sensitivity: 0.89, specificity: 0.98).

  10. Acne image analysis: lesion localization and classification

    NASA Astrophysics Data System (ADS)

    Abas, Fazly Salleh; Kaffenberger, Benjamin; Bikowski, Joseph; Gurcan, Metin N.

    2016-03-01

    Acne is a common skin condition present predominantly in the adolescent population, but may continue into adulthood. Scarring occurs commonly as a sequel to severe inflammatory acne. The presence of acne and resultant scars are more than cosmetic, with a significant potential to alter quality of life and even job prospects. The psychosocial effects of acne and scars can be disturbing and may be a risk factor for serious psychological concerns. Treatment efficacy is generally determined based on an invalidated gestalt by the physician and patient. However, the validated assessment of acne can be challenging and time consuming. Acne can be classified into several morphologies including closed comedones (whiteheads), open comedones (blackheads), papules, pustules, cysts (nodules) and scars. For a validated assessment, the different morphologies need to be counted independently, a method that is far too time consuming considering the limited time available for a consultation. However, it is practical to record and analyze images since dermatologists can validate the severity of acne within seconds after uploading an image. This paper covers the processes of region-ofinterest determination using entropy-based filtering and thresholding as well acne lesion feature extraction. Feature extraction methods using discrete wavelet frames and gray-level co-occurence matrix were presented and their effectiveness in separating the six major acne lesion classes were discussed. Several classifiers were used to test the extracted features. Correct classification accuracy as high as 85.5% was achieved using the binary classification tree with fourteen principle components used as descriptors. Further studies are underway to further improve the algorithm performance and validate it on a larger database.

  11. Segmentation and Classification of Bone Marrow Cells Images Using Contextual Information for Medical Diagnosis of Acute Leukemias.

    PubMed

    Reta, Carolina; Altamirano, Leopoldo; Gonzalez, Jesus A; Diaz-Hernandez, Raquel; Peregrina, Hayde; Olmos, Ivan; Alonso, Jose E; Lobato, Ruben

    2015-01-01

    Morphological identification of acute leukemia is a powerful tool used by hematologists to determine the family of such a disease. In some cases, experienced physicians are even able to determine the leukemia subtype of the sample. However, the identification process may have error rates up to 40% (when classifying acute leukemia subtypes) depending on the physician's experience and the sample quality. This problem raises the need to create automatic tools that provide hematologists with a second opinion during the classification process. Our research presents a contextual analysis methodology for the detection of acute leukemia subtypes from bone marrow cells images. We propose a cells separation algorithm to break up overlapped regions. In this phase, we achieved an average accuracy of 95% in the evaluation of the segmentation process. In a second phase, we extract descriptive features to the nucleus and cytoplasm obtained in the segmentation phase in order to classify leukemia families and subtypes. We finally created a decision algorithm that provides an automatic diagnosis for a patient. In our experiments, we achieved an overall accuracy of 92% in the supervised classification of acute leukemia families, 84% for the lymphoblastic subtypes, and 92% for the myeloblastic subtypes. Finally, we achieved accuracies of 95% in the diagnosis of leukemia families and 90% in the diagnosis of leukemia subtypes.

  12. Segmentation and Classification of Bone Marrow Cells Images Using Contextual Information for Medical Diagnosis of Acute Leukemias

    PubMed Central

    Reta, Carolina; Altamirano, Leopoldo; Gonzalez, Jesus A.; Diaz-Hernandez, Raquel; Peregrina, Hayde; Olmos, Ivan; Alonso, Jose E.; Lobato, Ruben

    2015-01-01

    Morphological identification of acute leukemia is a powerful tool used by hematologists to determine the family of such a disease. In some cases, experienced physicians are even able to determine the leukemia subtype of the sample. However, the identification process may have error rates up to 40% (when classifying acute leukemia subtypes) depending on the physician’s experience and the sample quality. This problem raises the need to create automatic tools that provide hematologists with a second opinion during the classification process. Our research presents a contextual analysis methodology for the detection of acute leukemia subtypes from bone marrow cells images. We propose a cells separation algorithm to break up overlapped regions. In this phase, we achieved an average accuracy of 95% in the evaluation of the segmentation process. In a second phase, we extract descriptive features to the nucleus and cytoplasm obtained in the segmentation phase in order to classify leukemia families and subtypes. We finally created a decision algorithm that provides an automatic diagnosis for a patient. In our experiments, we achieved an overall accuracy of 92% in the supervised classification of acute leukemia families, 84% for the lymphoblastic subtypes, and 92% for the myeloblastic subtypes. Finally, we achieved accuracies of 95% in the diagnosis of leukemia families and 90% in the diagnosis of leukemia subtypes. PMID:26107374

  13. Multiclass primal support vector machines for breast density classification.

    PubMed

    Land, Walker H; Verheggen, Elizabeth A

    2009-01-01

    Parenchymal patterns defining the density of breast tissue are detected by advanced correlation pattern recognition in an integrated Computer-Aided Detection (CAD) and diagnosis system. Fractal signatures of density are modelled according to four clinical categories. A Support Vector Machine (SVM) in the primal formulation solves the multiclass problem using 'One-Versus-All' (OVA) and 'All-Versus-All' (AVA) decompositions, achieving 85% and 94% accuracy, respectively. Fully automated classification of breast density via a texture model derived from fractal dimension, dispersion, and lacunarity moves current qualitative methods forward to objective quantitative measures, amenable with the overarching vision of substantiating the role of density in epidemiological risk models of breast cancer. PMID:20054985

  14. Motion data classification on the basis of dynamic time warping with a cloud point distance measure

    NASA Astrophysics Data System (ADS)

    Switonski, Adam; Josinski, Henryk; Zghidi, Hafedh; Wojciechowski, Konrad

    2016-06-01

    The paper deals with the problem of classification of model free motion data. The nearest neighbors classifier which is based on comparison performed by Dynamic Time Warping transform with cloud point distance measure is proposed. The classification utilizes both specific gait features reflected by a movements of subsequent skeleton joints and anthropometric data. To validate proposed approach human gait identification challenge problem is taken into consideration. The motion capture database containing data of 30 different humans collected in Human Motion Laboratory of Polish-Japanese Academy of Information Technology is used. The achieved results are satisfactory, the obtained accuracy of human recognition exceeds 90%. What is more, the applied cloud point distance measure does not depend on calibration process of motion capture system which results in reliable validation.

  15. Binary tree of SVM: a new fast multiclass training and classification algorithm.

    PubMed

    Fei, Ben; Liu, Jinbai

    2006-05-01

    We present a new architecture named Binary Tree of support vector machine (SVM), or BTS, in order to achieve high classification efficiency for multiclass problems. BTS and its enhanced version, c-BTS, decrease the number of binary classifiers to the greatest extent without increasing the complexity of the original problem. In the training phase, BTS has N - 1 binary classifiers in the best situation (N is the number of classes), while it has log4/3 ((N + 3)/4) binary tests on average when making a decision. At the same time the upper bound of convergence complexity is determined. The experiments in this paper indicate that maintaining comparable accuracy, BTS is much faster to be trained than other methods. Especially in classification, due to its Log complexity, it is much faster than directed acyclic graph SVM (DAGSVM) and ECOC in problems that have big class number.

  16. A novel classification method based on ICA and ELM: a case study in lie detection.

    PubMed

    Xiong, Yijun; Luo, Yu; Huang, Wentao; Zhang, Wenjia; Yang, Yong; Gao, Junfeng

    2014-01-01

    The classification of EEG tasks has drawn much attention in recent years. In this paper, a novel classification model based on independent component analysis (ICA) and Extreme learning machine (ELM) is proposed to detect lying. Firstly, ICA and its topography information were used to automatically identify the P300 ICs. Then, time and frequency-domain features were extracted from the reconstructed P3 waveforms. Finally, two classes of feature samples were used to train ELM, Back-propagation network (BPNN) and support vector machine (SVM) classifiers for comparison. The optimal number of P3 ICs and the values of classifier parameter were optimized by the cross-validation procedures. Experimental results show that the presented method (ICA_ELM) achieves the highest training accuracy of 95.40% with extremely less training and testing time on detecting P3 components for the guilty and the innocent subjects. The results indicate that the proposed method can be applied in lie detection.

  17. Study of USGS/NASA land use classification system. [computer analysis from LANDSAT data

    NASA Technical Reports Server (NTRS)

    Spann, G. W.

    1975-01-01

    The results of a computer mapping project using LANDSAT data and the USGS/NASA land use classification system are summarized. During the computer mapping portion of the project, accuracies of 67 percent to 79 percent were achieved using Level II of the classification system and a 4,000 acre test site centered on Douglasville, Georgia. Analysis of response to a questionaire circulated to actual and potential LANDSAT data users reveals several important findings: (1) there is a substantial desire for additional information related to LANDSAT capabilities; (2) a majority of the respondents feel computer mapping from LANDSAT data could aid present or future projects; and (3) the costs of computer mapping are substantially less than those of other methods.

  18. A novel classification method based on ICA and ELM: a case study in lie detection.

    PubMed

    Xiong, Yijun; Luo, Yu; Huang, Wentao; Zhang, Wenjia; Yang, Yong; Gao, Junfeng

    2014-01-01

    The classification of EEG tasks has drawn much attention in recent years. In this paper, a novel classification model based on independent component analysis (ICA) and Extreme learning machine (ELM) is proposed to detect lying. Firstly, ICA and its topography information were used to automatically identify the P300 ICs. Then, time and frequency-domain features were extracted from the reconstructed P3 waveforms. Finally, two classes of feature samples were used to train ELM, Back-propagation network (BPNN) and support vector machine (SVM) classifiers for comparison. The optimal number of P3 ICs and the values of classifier parameter were optimized by the cross-validation procedures. Experimental results show that the presented method (ICA_ELM) achieves the highest training accuracy of 95.40% with extremely less training and testing time on detecting P3 components for the guilty and the innocent subjects. The results indicate that the proposed method can be applied in lie detection. PMID:24211917

  19. Computers vs. Humans in Galaxy Classification

    NASA Astrophysics Data System (ADS)

    Kohler, Susanna

    2016-04-01

    54% for spirals and 80% for ellipticals, the agreement is over 98%. [Kuminski et al. 2016]In addition, the classifier calculates a certainty level for each classification, with the certainties adding to 100%: a galaxy categorized as spiral at 85% certainty is categorized as elliptical at 15% certainty. This provides a quantity/quality tradeoff, allowing for the creation of subcatalogs by cutting at specific certainty levels. Selecting for a high level of certainty decreases the sample size, but increases the samples classification accuracy.Comparing the OutcomeTo evaluate the accuracy of the algorithms findings, the authors examined SDSS galaxies that had also been classified by Galaxy Zoo. In particular, they used a 45,000-galaxy subset that consists only of superclean Galaxy Zoo galaxies meaning the human volunteers who categorized them were in agreement at a level of 95% or higher.Number of spiral and elliptical galaxies classified above different certainty levels. Cutting at the 54% certainty level for spirals and 80% for ellipticals leaves ~900,000 and ~600,000 spiral and elliptical galaxies, respectively. [Kuminski et al. 2016]In this set, Kuminski and Shamir found that if they draw a cut-off at the 54% certainty level for spiral galaxies and the 80% certainty level for ellipticals, they find 98% agreement between the computer classification of the galaxies and the human classification via Galaxy Zoo. Applying these cuts to the entire sample resulted in the identification of ~900,000 spiral galaxies and ~600,000 ellipticals, representing the largest catalog of its kind.The authors acknowledge that completeness is a problem; half the data had to be cut to achieve this level of accuracy. Sacrificing some data can still result in very large catalogs, however and as surveys become more powerful and large databases become more prevalent, algorithms such as this one will likely become critical to the scientific process.CitationEvan Kuminski and Lior Shamir 2016 Ap

  20. Intelligent feature selection techniques for pattern classification of Lamb wave signals

    SciTech Connect

    Hinders, Mark K.; Miller, Corey A.

    2014-02-18

    Lamb wave interaction with flaws is a complex, three-dimensional phenomenon, which often frustrates signal interpretation schemes based on mode arrival time shifts predicted by dispersion curves. As the flaw severity increases, scattering and mode conversion effects will often dominate the time-domain signals, obscuring available information about flaws because multiple modes may arrive on top of each other. Even for idealized flaw geometries the scattering and mode conversion behavior of Lamb waves is very complex. Here, multi-mode Lamb waves in a metal plate are propagated across a rectangular flat-bottom hole in a sequence of pitch-catch measurements corresponding to the double crosshole tomography geometry. The flaw is sequentially deepened, with the Lamb wave measurements repeated at each flaw depth. Lamb wave tomography reconstructions are used to identify which waveforms have interacted with the flaw and thereby carry information about its depth. Multiple features are extracted from each of the Lamb wave signals using wavelets, which are then fed to statistical pattern classification algorithms that identify flaw severity. In order to achieve the highest classification accuracy, an optimal feature space is required but it’s never known a priori which features are going to be best. For structural health monitoring we make use of the fact that physical flaws, such as corrosion, will only increase over time. This allows us to identify feature vectors which are topologically well-behaved by requiring that sequential classes “line up” in feature vector space. An intelligent feature selection routine is illustrated that identifies favorable class distributions in multi-dimensional feature spaces using computational homology theory. Betti numbers and formal classification accuracies are calculated for each feature space subset to establish a correlation between the topology of the class distribution and the corresponding classification accuracy.

  1. Intelligent feature selection techniques for pattern classification of Lamb wave signals

    NASA Astrophysics Data System (ADS)

    Hinders, Mark K.; Miller, Corey A.

    2014-02-01

    Lamb wave interaction with flaws is a complex, three-dimensional phenomenon, which often frustrates signal interpretation schemes based on mode arrival time shifts predicted by dispersion curves. As the flaw severity increases, scattering and mode conversion effects will often dominate the time-domain signals, obscuring available information about flaws because multiple modes may arrive on top of each other. Even for idealized flaw geometries the scattering and mode conversion behavior of Lamb waves is very complex. Here, multi-mode Lamb waves in a metal plate are propagated across a rectangular flat-bottom hole in a sequence of pitch-catch measurements corresponding to the double crosshole tomography geometry. The flaw is sequentially deepened, with the Lamb wave measurements repeated at each flaw depth. Lamb wave tomography reconstructions are used to identify which waveforms have interacted with the flaw and thereby carry information about its depth. Multiple features are extracted from each of the Lamb wave signals using wavelets, which are then fed to statistical pattern classification algorithms that identify flaw severity. In order to achieve the highest classification accuracy, an optimal feature space is required but it's never known a priori which features are going to be best. For structural health monitoring we make use of the fact that physical flaws, such as corrosion, will only increase over time. This allows us to identify feature vectors which are topologically well-behaved by requiring that sequential classes "line up" in feature vector space. An intelligent feature selection routine is illustrated that identifies favorable class distributions in multi-dimensional feature spaces using computational homology theory. Betti numbers and formal classification accuracies are calculated for each feature space subset to establish a correlation between the topology of the class distribution and the corresponding classification accuracy.

  2. Classification of visual and linguistic tasks using eye-movement features.

    PubMed

    Coco, Moreno I; Keller, Frank

    2014-03-07

    The role of the task has received special attention in visual-cognition research because it can provide causal explanations of goal-directed eye-movement responses. The dependency between visual attention and task suggests that eye movements can be used to classify the task being performed. A recent study by Greene, Liu, and Wolfe (2012), however, fails to achieve accurate classification of visual tasks based on eye-movement features. In the present study, we hypothesize that tasks can be successfully classified when they differ with respect to the involvement of other cognitive domains, such as language processing. We extract the eye-movement features used by Greene et al. as well as additional features from the data of three different tasks: visual search, object naming, and scene description. First, we demonstrated that eye-movement responses make it possible to characterize the goals of these tasks. Then, we trained three different types of classifiers and predicted the task participants performed with an accuracy well above chance (a maximum of 88% for visual search). An analysis of the relative importance of features for classification accuracy reveals that just one feature, i.e., initiation time, is sufficient for above-chance performance (a maximum of 79% accuracy in object naming). Crucially, this feature is independent of task duration, which differs systematically across the three tasks we investigated. Overall, the best task classification performance was obtained with a set of seven features that included both spatial information (e.g., entropy of attention allocation) and temporal components (e.g., total fixation on objects) of the eye-movement record. This result confirms the task-dependent allocation of visual attention and extends previous work by showing that task classification is possible when tasks differ in the cognitive processes involved (purely visual tasks such as search vs. communicative tasks such as scene description).

  3. A new classification scheme of plastic wastes based upon recycling labels.

    PubMed

    Özkan, Kemal; Ergin, Semih; Işık, Şahin; Işıklı, Idil

    2015-01-01

    Since recycling of materials is widely assumed to be environmentally and economically beneficial, reliable sorting and processing of waste packaging materials such as plastics is very important for recycling with high efficiency. An automated system that can quickly categorize these materials is certainly needed for obtaining maximum classification while maintaining high throughput. In this paper, first of all, the photographs of the plastic bottles have been taken and several preprocessing steps were carried out. The first preprocessing step is to extract the plastic area of a bottle from the background. Then, the morphological image operations are implemented. These operations are edge detection, noise removal, hole removing, image enhancement, and image segmentation. These morphological operations can be generally defined in terms of the combinations of erosion and dilation. The effect of bottle color as well as label are eliminated using these operations. Secondly, the pixel-wise intensity values of the plastic bottle images have been used together with the most popular subspace and statistical feature extraction methods to construct the feature vectors in this study. Only three types of plastics are considered due to higher existence ratio of them than the other plastic types in the world. The decision mechanism consists of five different feature extraction methods including as Principal Component Analysis (PCA), Kernel PCA (KPCA), Fisher's Linear Discriminant Analysis (FLDA), Singular Value Decomposition (SVD) and Laplacian Eigenmaps (LEMAP) and uses a simple experimental setup with a camera and homogenous backlighting. Due to the giving global solution for a classification problem, Support Vector Machine (SVM) is selected to achieve the classification task and majority voting technique is used as the decision mechanism. This technique equally weights each classification result and assigns the given plastic object to the class that the most classification

  4. A new classification scheme of plastic wastes based upon recycling labels.

    PubMed

    Özkan, Kemal; Ergin, Semih; Işık, Şahin; Işıklı, Idil

    2015-01-01

    Since recycling of materials is widely assumed to be environmentally and economically beneficial, reliable sorting and processing of waste packaging materials such as plastics is very important for recycling with high efficiency. An automated system that can quickly categorize these materials is certainly needed for obtaining maximum classification while maintaining high throughput. In this paper, first of all, the photographs of the plastic bottles have been taken and several preprocessing steps were carried out. The first preprocessing step is to extract the plastic area of a bottle from the background. Then, the morphological image operations are implemented. These operations are edge detection, noise removal, hole removing, image enhancement, and image segmentation. These morphological operations can be generally defined in terms of the combinations of erosion and dilation. The effect of bottle color as well as label are eliminated using these operations. Secondly, the pixel-wise intensity values of the plastic bottle images have been used together with the most popular subspace and statistical feature extraction methods to construct the feature vectors in this study. Only three types of plastics are considered due to higher existence ratio of them than the other plastic types in the world. The decision mechanism consists of five different feature extraction methods including as Principal Component Analysis (PCA), Kernel PCA (KPCA), Fisher's Linear Discriminant Analysis (FLDA), Singular Value Decomposition (SVD) and Laplacian Eigenmaps (LEMAP) and uses a simple experimental setup with a camera and homogenous backlighting. Due to the giving global solution for a classification problem, Support Vector Machine (SVM) is selected to achieve the classification task and majority voting technique is used as the decision mechanism. This technique equally weights each classification result and assigns the given plastic object to the class that the most classification

  5. Contextualizing Object Detection and Classification.

    PubMed

    Chen, Qiang; Song, Zheng; Dong, Jian; Huang, Zhongyang; Hua, Yang; Yan, Shuicheng

    2015-01-01

    We investigate how to iteratively and mutually boost object classification and detection performance by taking the outputs from one task as the context of the other one. While context models have been quite popular, previous works mainly concentrate on co-occurrence relationship within classes and few of them focus on contextualization from a top-down perspective, i.e. high-level task context. In this paper, our system adopts a new method for adaptive context modeling and iterative boosting. First, the contextualized support vector machine (Context-SVM) is proposed, where the context takes the role of dynamically adjusting the classification score based on the sample ambiguity, and thus the context-adaptive classifier is achieved. Then, an iterative training procedure is presented. In each step, Context-SVM, associated with the output context from one task (object classification or detection), is instantiated to boost the performance for the other task, whose augmented outputs are then further used to improve the former task by Context-SVM. The proposed solution is evaluated on the object classification and detection tasks of PASCAL Visual Object Classes Challenge (VOC) 2007, 2010 and SUN09 data sets, and achieves the state-of-the-art performance.

  6. View-centralized multi-atlas classification for Alzheimer's disease diagnosis.

    PubMed

    Liu, Mingxia; Zhang, Daoqiang; Shen, Dinggang

    2015-05-01

    Multi-atlas based methods have been recently used for classification of Alzheimer's disease (AD) and its prodromal stage, that is, mild cognitive impairment (MCI). Compared with traditional single-atlas based methods, multiatlas based methods adopt multiple predefined atlases and thus are less biased by a certain atlas. However, most existing multiatlas based methods simply average or concatenate the features from multiple atlases, which may ignore the potentially important diagnosis information related to the anatomical differences among different atlases. In this paper, we propose a novel view (i.e., atlas) centralized multi-atlas classification method, which can better exploit useful information in multiple feature representations from different atlases. Specifically, all brain images are registered onto multiple atlases individually, to extract feature representations in each atlas space. Then, the proposed view-centralized multi-atlas feature selection method is used to select the most discriminative features from each atlas with extra guidance from other atlases. Next, we design a support vector machine (SVM) classifier using the selected features in each atlas space. Finally, we combine multiple SVM classifiers for multiple atlases through a classifier ensemble strategy for making a final decision. We have evaluated our method on 459 subjects [including 97 AD, 117 progressive MCI (p-MCI), 117 stable MCI (s-MCI), and 128 normal controls (NC)] from the Alzheimer's Disease Neuroimaging Initiative database, and achieved an accuracy of 92.51% for AD versus NC classification and an accuracy of 78.88% for p-MCI versus s-MCI classification. These results demonstrate that the proposed method can significantly outperform the previous multi-atlas based classification methods.

  7. Classification of Photogrammetric Point Clouds of Scaffolds for Construction Site Monitoring Using Subspace Clustering and PCA

    NASA Astrophysics Data System (ADS)

    Xu, Y.; Tuttas, S.; Heogner, L.; Stilla, U.

    2016-06-01

    This paper presents an approach for the classification of photogrammetric point clouds of scaffolding components in a construction site, aiming at making a preparation for the automatic monitoring of construction site by reconstructing an as-built Building Information Model (as-built BIM). The points belonging to tubes and toeboards of scaffolds will be distinguished via subspace clustering process and principal components analysis (PCA) algorithm. The overall workflow includes four essential processing steps. Initially, the spherical support region of each point is selected. In the second step, the normalized cut algorithm based on spectral clustering theory is introduced for the subspace clustering, so as to select suitable subspace clusters of points and avoid outliers. Then, in the third step, the feature of each point is calculated by measuring distances between points and the plane of local reference frame defined by PCA in cluster. Finally, the types of points are distinguished and labelled through a supervised classification method, with random forest algorithm used. The effectiveness and applicability of the proposed steps are investigated in both simulated test data and real scenario. The results obtained by the two experiments reveal that the proposed approaches are qualified to the classification of points belonging to linear shape objects having different shapes of sections. For the tests using synthetic point cloud, the classification accuracy can reach 80%, with the condition contaminated by noise and outliers. For the application in real scenario, our method can also achieve a classification accuracy of better than 63%, without using any information about the normal vector of local surface.

  8. Robotic Rock Classification

    NASA Technical Reports Server (NTRS)

    Hebert, Martial

    1999-01-01

    This report describes a three-month research program undertook jointly by the Robotics Institute at Carnegie Mellon University and Ames Research Center as part of the Ames' Joint Research Initiative (JRI.) The work was conducted at the Ames Research Center by Mr. Liam Pedersen, a graduate student in the CMU Ph.D. program in Robotics under the supervision Dr. Ted Roush at the Space Science Division of the Ames Research Center from May 15 1999 to August 15, 1999. Dr. Martial Hebert is Mr. Pedersen's research adviser at CMU and is Principal Investigator of this Grant. The goal of this project is to investigate and implement methods suitable for a robotic rover to autonomously identify rocks and minerals in its vicinity, and to statistically characterize the local geological environment. Although primary sensors for these tasks are a reflection spectrometer and color camera, the goal is to create a framework under which data from multiple sensors, and multiple readings on the same object, can be combined in a principled manner. Furthermore, it is envisioned that knowledge of the local area, either a priori or gathered by the robot, will be used to improve classification accuracy. The key results obtained during this project are: The continuation of the development of a rock classifier; development of theoretical statistical methods; development of methods for evaluating and selecting sensors; and experimentation with data mining techniques on the Ames spectral library. The results of this work are being applied at CMU, in particular in the context of the Winter 99 Antarctica expedition in which the classification techniques will be used on the Nomad robot. Conversely, the software developed based on those techniques will continue to be made available to NASA Ames and the data collected from the Nomad experiments will also be made available.

  9. Multiple Spectral-Spatial Classification Approach for Hyperspectral Data

    NASA Technical Reports Server (NTRS)

    Tarabalka, Yuliya; Benediktsson, Jon Atli; Chanussot, Jocelyn; Tilton, James C.

    2010-01-01

    A .new multiple classifier approach for spectral-spatial classification of hyperspectral images is proposed. Several classifiers are used independently to classify an image. For every pixel, if all the classifiers have assigned this pixel to the same class, the pixel is kept as a marker, i.e., a seed of the spatial region, with the corresponding class label. We propose to use spectral-spatial classifiers at the preliminary step of the marker selection procedure, each of them combining the results of a pixel-wise classification and a segmentation map. Different segmentation methods based on dissimilar principles lead to different classification results. Furthermore, a minimum spanning forest is built, where each tree is rooted on a classification -driven marker and forms a region in the spectral -spatial classification: map. Experimental results are presented for two hyperspectral airborne images. The proposed method significantly improves classification accuracies, when compared to previously proposed classification techniques.

  10. Comparative performance of ALOS PALSAR polarization bands and its combination with ALOS AVNIR-2 data for land cover classification

    NASA Astrophysics Data System (ADS)

    Sim, C. K.; Abdullah, K.; MatJafri, M. Z.; Lim, H. S.

    2014-02-01

    Microwave Remote Sensing data have been widely used for land cover classification in our environment. In this study, ALOS PALSAR polarization bands were used to identify land cover features in three study areas in Malaysia. The study area consists of Penang, Perak and Kedah. The aims of this research are to investigate the performance of ALOS PALSAR datasets which are assessed independently and combination of these data with ALOS AVNIR-2 for land cover classification. Standard supervised classification method Maximum Likelihood Classifier (MLC) was applied. Various land cover classes were identified and assessed using the Transformed Divergence (TD) separability measures. The PALSAR data training areas were chosen based on the information obtained from ALOS AVNIR-2 datasets. The original data gave very poor results in identifying land cover classes due to the presence of immense speckle. The extraction and use of mean texture measures was found to be very advantageous when evaluating the separability among the different land covers. Hence, mean texture was capable to provide higher classification accuracies as compared to the original radar. The highest overall accuracy was achieved by combining the radar mean texture with ALOS AVNIR-2 data. This study proved that the land cover of Penang, Perak, and Kedah can be mapped accurately using combination of optical and radar data.

  11. Multiclass Classification by Adaptive Network of Dendritic Neurons with Binary Synapses Using Structural Plasticity

    PubMed Central

    Hussain, Shaista; Basu, Arindam

    2016-01-01

    The development of power-efficient neuromorphic devices presents the challenge of designing spike pattern classification algorithms which can be implemented on low-precision hardware and can also achieve state-of-the-art performance. In our pursuit of meeting this challenge, we present a pattern classification model which uses a sparse connection matrix and exploits the mechanism of nonlinear dendritic processing to achieve high classification accuracy. A rate-based structural learning rule for multiclass classification is proposed which modifies a connectivity matrix of binary synaptic connections by choosing the best “k” out of “d” inputs to make connections on every dendritic branch (k < < d). Because learning only modifies connectivity, the model is well suited for implementation in neuromorphic systems using address-event representation (AER). We develop an ensemble method which combines several dendritic classifiers to achieve enhanced generalization over individual classifiers. We have two major findings: (1) Our results demonstrate that an ensemble created with classifiers comprising moderate number of dendrites performs better than both ensembles of perceptrons and of complex dendritic trees. (2) In order to determine the moderate number of dendrites required for a specific classification problem, a two-step solution is proposed. First, an adaptive approach is proposed which scales the relative size of the dendritic trees of neurons for each class. It works by progressively adding dendrites with fixed number of synapses to the network, thereby allocating synaptic resources as per the complexity of the given problem. As a second step, theoretical capacity calculations are used to convert each neuronal dendritic tree to its optimal topology where dendrites of each class are assigned different number of synapses. The performance of the model is evaluated on classification of handwritten digits from the benchmark MNIST dataset and compared with other

  12. Multiclass Classification by Adaptive Network of Dendritic Neurons with Binary Synapses Using Structural Plasticity.

    PubMed

    Hussain, Shaista; Basu, Arindam

    2016-01-01

    The development of power-efficient neuromorphic devices presents the challenge of designing spike pattern classification algorithms which can be implemented on low-precision hardware and can also achieve state-of-the-art performance. In our pursuit of meeting this challenge, we present a pattern classification model which uses a sparse connection matrix and exploits the mechanism of nonlinear dendritic processing to achieve high classification accuracy. A rate-based structural learning rule for multiclass classification is proposed which modifies a connectivity matrix of binary synaptic connections by choosing the best "k" out of "d" inputs to make connections on every dendritic branch (k < < d). Because learning only modifies connectivity, the model is well suited for implementation in neuromorphic systems using address-event representation (AER). We develop an ensemble method which combines several dendritic classifiers to achieve enhanced generalization over individual classifiers. We have two major findings: (1) Our results demonstrate that an ensemble created with classifiers comprising moderate number of dendrites performs better than both ensembles of perceptrons and of complex dendritic trees. (2) In order to determine the moderate number of dendrites required for a specific classification problem, a two-step solution is proposed. First, an adaptive approach is proposed which scales the relative size of the dendritic trees of neurons for each class. It works by progressively adding dendrites with fixed number of synapses to the network, thereby allocating synaptic resources as per the complexity of the given problem. As a second step, theoretical capacity calculations are used to convert each neuronal dendritic tree to its optimal topology where dendrites of each class are assigned different number of synapses. The performance of the model is evaluated on classification of handwritten digits from the benchmark MNIST dataset and compared with other spike

  13. Multiclass Classification by Adaptive Network of Dendritic Neurons with Binary Synapses Using Structural Plasticity.

    PubMed

    Hussain, Shaista; Basu, Arindam

    2016-01-01

    The development of power-efficient neuromorphic devices presents the challenge of designing spike pattern classification algorithms which can be implemented on low-precision hardware and can also achieve state-of-the-art performance. In our pursuit of meeting this challenge, we present a pattern classification model which uses a sparse connection matrix and exploits the mechanism of nonlinear dendritic processing to achieve high classification accuracy. A rate-based structural learning rule for multiclass classification is proposed which modifies a connectivity matrix of binary synaptic connections by choosing the best "k" out of "d" inputs to make connections on every dendritic branch (k < < d). Because learning only modifies connectivity, the model is well suited for implementation in neuromorphic systems using address-event representation (AER). We develop an ensemble method which combines several dendritic classifiers to achieve enhanced generalization over individual classifiers. We have two major findings: (1) Our results demonstrate that an ensemble created with classifiers comprising moderate number of dendrites performs better than both ensembles of perceptrons and of complex dendritic trees. (2) In order to determine the moderate number of dendrites required for a specific classification problem, a two-step solution is proposed. First, an adaptive approach is proposed which scales the relative size of the dendritic trees of neurons for each class. It works by progressively adding dendrites with fixed number of synapses to the network, thereby allocating synaptic resources as per the complexity of the given problem. As a second step, theoretical capacity calculations are used to convert each neuronal dendritic tree to its optimal topology where dendrites of each class are assigned different number of synapses. The performance of the model is evaluated on classification of handwritten digits from the benchmark MNIST dataset and compared with other spike

  14. Ultrahigh accuracy imaging modality for super-localization microscopy.

    PubMed

    Chao, Jerry; Ram, Sripad; Ward, E Sally; Ober, Raimund J

    2013-04-01

    Super-localization microscopy encompasses techniques that depend on the accurate localization of individual molecules from generally low-light images. The obtainable localization accuracies, however, are ultimately limited by the image detector's pixelation and noise. We present the ultrahigh accuracy imaging modality (UAIM), which allows users to obtain accuracies approaching the accuracy that is achievable only in the absence of detector pixelation and noise, and which we found can experimentally provide a >200% accuracy improvement over conventional low-light imaging. PMID:23455923

  15. Anterior chamber angle classification using multiscale histograms of oriented gradients for glaucoma subtype identification.

    PubMed

    Xu, Yanwu; Liu, Jiang; Tan, Ngan Meng; Lee, Beng Hai; Wong, Damon Wing Kee; Baskaran, Mani; Perera, Shamira A; Aung, Tin

    2012-01-01

    Glaucoma subtype can be identified according to the configuration of the anterior chamber angle(ACA). In this paper, we present an ACA classification approach based on histograms of oriented gradients at multiple scales. In digital optical coherence tomography (OCT) photographs, our method automatically localizes the ACA, and extracts histograms of oriented gradients (HOG) features from this region to classify the angle as an open angle (OA) or an angle-closure(AC). This proposed method has three major features that differs from existing methods. First, the ACA localization from OCT images is fully automated and efficient for different ACA configurations. Second, the ACA is directly classified as OA/AC by using multiscale HOG visual features only, which is different from previous ACA assessment approaches that on clinical features. Third, it demonstrates that visual features with higher dimensions outperform low dimensional clinical features in terms of angle closure classification accuracy. Testing was performed on a large clinical dataset, comprising of 2048 images. The proposed method achieves a 0.835±0.068 AUC value and 75.8% ± 6.4% balanced accuracy at a 85% specificity, which outperforms existing ACA classification approaches based on clinical features.

  16. Cross-correlation of EEG frequency bands and heart rate variability for sleep apnoea classification.

    PubMed

    Abdullah, Haslaile; Maddage, Namunu C; Cosic, Irena; Cvetkovic, Dean

    2010-12-01

    Sleep apnoea is a sleep breathing disorder which causes changes in cardiac and neuronal activity and discontinuities in sleep pattern when observed via electrocardiogram (ECG) and electroencephalogram (EEG). Using both statistical analysis and Gaussian discriminative modelling approaches, this paper presents a pilot study of assessing the cross-correlation between EEG frequency bands and heart rate variability (HRV) in normal and sleep apnoea clinical patients. For the study we used EEG (delta, theta, alpha, sigma and beta) and HRV (LF(nu), HF(nu) and LF/HF) features from the spectral analysis. The statistical analysis in different sleep stages highlighted that in sleep apnoea patients, the EEG delta, sigma and beta bands exhibited a strong correlation with HRV features. Then the correlation between EEG frequency bands and HRV features were examined for sleep apnoea classification using univariate and multivariate Gaussian models (UGs and MGs). The MG outperformed the UG in the classification. When EEG and HRV features were combined and modelled with MG, we achieved 64% correct classification accuracy, which is 2 or 8% improvement with respect to using only EEG or ECG features. When delta and acceleration coefficients of the EEG features were incorporated, then the overall accuracy improved to 71%.

  17. Probability-based differential normalized fluorescence bivariate analysis for the classification of tissue autofluorescence spectra.

    PubMed

    Wang, Gufeng; Platz, Charles P; Geng, M Lei

    2006-05-01

    Differential normalized fluorescence (DNF) is an efficient and effective method for the differentiation of normal and cancerous tissue fluorescence spectra. The diagnostic features are extracted from the difference between the averaged cancerous and averaged normal tissue spectra and used as indices in tissue classification. In this paper, a new method, probability-based DNF bivariate analysis, is introduced based on the univariate DNF method. Two differentiation features are used concurrently in the new method to achieve better classification accuracy. The probability of each sample belonging to a disease state is determined with Bayes decision theory. This probability approach classifies the tissue spectra according to disease states and provides uncertainty information on classification. With a data set of 57 colonic tissue sites, probability-based DNF bivariate analysis is demonstrated to improve the accuracy of cancer diagnosis. The bivariate DNF analysis only requires the collection of a few data points across the entire emission spectrum and has the potential of improving data acquisition speed in tissue imaging.

  18. Classification Consistency when Scores Are Converted to Grades: Examination Marks versus Moderated School Assessments

    ERIC Educational Resources Information Center

    MacCann, Robert G.; Stanley, Gordon

    2010-01-01

    In educational systems, concern has been expressed about the accuracy of classification when marks are aligned to grades or levels. In particular, it has been claimed that a school assessment-based grading would have much greater levels of accuracy than one based on examination scores. This paper investigates classification consistency by…

  19. High accuracy OMEGA timekeeping

    NASA Technical Reports Server (NTRS)

    Imbier, E. A.

    1982-01-01

    The Smithsonian Astrophysical Observatory (SAO) operates a worldwide satellite tracking network which uses a combination of OMEGA as a frequency reference, dual timing channels, and portable clock comparisons to maintain accurate epoch time. Propagational charts from the U.S. Coast Guard OMEGA monitor program minimize diurnal and seasonal effects. Daily phase value publications of the U.S. Naval Observatory provide corrections to the field collected timing data to produce an averaged time line comprised of straight line segments called a time history file (station clock minus UTC). Depending upon clock location, reduced time data accuracies of between two and eight microseconds are typical.

  20. Determining Optimal Feature-Combination for LDA Classification of Functional Near-Infrared Spectroscopy Signals in Brain-Computer Interface Application

    PubMed Central

    Naseer, Noman; Noori, Farzan M.; Qureshi, Nauman K.; Hong, Keum-Shik

    2016-01-01

    In this study, we determine the optimal feature-combination for classification of functional near-infrared spectroscopy (fNIRS) signals with the best accuracies for development of a two-class brain-computer interface (BCI). Using a multi-channel continuous-wave imaging system, mental arithmetic signals are acquired from the prefrontal cortex of seven healthy subjects. After removing physiological noises, six oxygenated and deoxygenated hemoglobin (HbO and HbR) features—mean, slope, variance, peak, skewness and kurtosis—are calculated. All possible 2- and 3-feature combinations of the calculated features are then used to classify mental arithmetic vs. rest using linear discriminant analysis (LDA). It is found that the combinations containing mean and peak values yielded significantly higher (p < 0.05) classification accuracies for both HbO and HbR than did all of the other combinations, across all of the subjects. These results demonstrate the feasibility of achieving high classification accuracies using mean and peak values of HbO and HbR as features for classification of mental arithmetic vs. rest for a two-class BCI. PMID:27252637

  1. Determining Optimal Feature-Combination for LDA Classification of Functional Near-Infrared Spectroscopy Signals in Brain-Computer Interface Application.

    PubMed

    Naseer, Noman; Noori, Farzan M; Qureshi, Nauman K; Hong, Keum-Shik

    2016-01-01

    In this study, we determine the optimal feature-combination for classification of functional near-infrared spectroscopy (fNIRS) signals with the best accuracies for development of a two-class brain-computer interface (BCI). Using a multi-channel continuous-wave imaging system, mental arithmetic signals are acquired from the prefrontal cortex of seven healthy subjects. After removing physiological noises, six oxygenated and deoxygenated hemoglobin (HbO and HbR) features-mean, slope, variance, peak, skewness and kurtosis-are calculated. All possible 2- and 3-feature combinations of the calculated features are then used to classify mental arithmetic vs. rest using linear discriminant analysis (LDA). It is found that the combinations containing mean and peak values yielded significantly higher (p < 0.05) classification accuracies for both HbO and HbR than did all of the other combinations, across all of the subjects. These results demonstrate the feasibility of achieving high classification accuracies using mean and peak values of HbO and HbR as features for classification of mental arithmetic vs. rest for a two-class BCI.

  2. Determining Optimal Feature-Combination for LDA Classification of Functional Near-Infrared Spectroscopy Signals in Brain-Computer Interface Application.

    PubMed

    Naseer, Noman; Noori, Farzan M; Qureshi, Nauman K; Hong, Keum-Shik

    2016-01-01

    In this study, we determine the optimal feature-combination for classification of functional near-infrared spectroscopy (fNIRS) signals with the best accuracies for development of a two-class brain-computer interface (BCI). Using a multi-channel continuous-wave imaging system, mental arithmetic signals are acquired from the prefrontal cortex of seven healthy subjects. After removing physiological noises, six oxygenated and deoxygenated hemoglobin (HbO and HbR) features-mean, slope, variance, peak, skewness and kurtosis-are calculated. All possible 2- and 3-feature combinations of the calculated features are then used to classify mental arithmetic vs. rest using linear discriminant analysis (LDA). It is found that the combinations containing mean and peak values yielded significantly higher (p < 0.05) classification accuracies for both HbO and HbR than did all of the other combinations, across all of the subjects. These results demonstrate the feasibility of achieving high classification accuracies using mean and peak values of HbO and HbR as features for classification of mental arithmetic vs. rest for a two-class BCI. PMID:27252637

  3. Comparison of statistical, LBP, and multi-resolution analysis features for breast mass classification.

    PubMed

    Reyad, Yasser A; Berbar, Mohamed A; Hussain, Muhammad

    2014-09-01

    Millions of women are suffering from breast cancer, which can be treated effectively if it is detected early. Mammography is broadly recognized as an effective imaging modality for the early detection of breast cancer. Computer-aided diagnosis (CAD) systems are very helpful for radiologists in detecting and diagnosing abnormalities earlier and faster than traditional screening programs. An important step of a CAD system is feature extraction. This research gives a comprehensive study of the effects of different features to be used in a CAD system for the classification of masses. The features are extracted using local binary pattern (LBP), which is a texture descriptor, statistical measures, and multi-resolution frameworks. Statistical and LBP features are extracted from each region of interest (ROI), taken from mammogram images, after dividing it into N×N blocks. The multi-resolution features are based on discrete wavelet transform (DWT) and contourlet transform (CT). In multi-resolution analysis, ROIs are decomposed into low sub-band and high sub-bands at different resolution levels and the coefficients of the low sub-band at the last level are taken as features. Support vector machines (SVM) is used for classification. The evaluation is performed using Digital Database for Screening Mammography (DDSM) database. An accuracy of 98.43 is obtained using statistical or LBP features but when both these types of features are fused, the accuracy is increased to 98.63. The CT features achieved classification accuracy of 98.43 whereas the accuracy resulted from DWT features is 96.93. The statistical analysis and ROC curves show that methods based on LBP, statistical measures and CT performs equally well and they not only outperform DWT based method but also other existing methods. PMID:25037713

  4. Land-cover classification in a moist tropical region of Brazil with Landsat TM imagery.

    PubMed

    Li, Guiying; Lu, Dengsheng; Moran, Emilio; Hetrick, Scott

    2011-01-01

    This research aims to improve land-cover classification accuracy in a moist tropical region in Brazil by examining the use of different remote sensing-derived variables and classification algorithms. Different scenarios based on Landsat Thematic Mapper (TM) spectral data and derived vegetation indices and textural images, and different classification algorithms - maximum likelihood classification (MLC), artificial neural network (ANN), classification tree analysis (CTA), and object-based classification (OBC), were explored. The results indicated that a combination of vegetation indices as extra bands into Landsat TM multispectral bands did not improve the overall classification performance, but the combination of textural images was valuable for improving vegetation classification accuracy. In particular, the combination of both vegetation indices and textural images into TM multispectral bands improved overall classification accuracy by 5.6% and kappa coefficient by 6.25%. Comparison of the different classification algorithms indicated that CTA and ANN have poor classification performance in this research, but OBC improved primary forest and pasture classification accuracies. This research indicates that use of textural images or use of OBC are especially valuable for improving the vegetation classes such as upland and liana forest classes having complex stand structures and having relatively large patch sizes.

  5. Spatial Classification of Orchards and Vineyards with High Spatial Resolution Panchromatic Imagery

    SciTech Connect

    Warner, Timothy; Steinmaus, Karen L.

    2005-02-01

    New high resolution single spectral band imagery offers the capability to conduct image classifications based on spatial patterns in imagery. A classification algorithm based on autocorrelation patterns was developed to automatically extract orchards and vineyards from satellite imagery. The algorithm was tested on IKONOS imagery over Granger, WA, which resulted in a classification accuracy of 95%.

  6. Towards Experimental Accuracy from the First Principles

    NASA Astrophysics Data System (ADS)

    Polyansky, O. L.; Lodi, L.; Tennyson, J.; Zobov, N. F.

    2013-06-01

    Producing ab initio ro-vibrational energy levels of small, gas-phase molecules with an accuracy of 0.10 cm^{-1} would constitute a significant step forward in theoretical spectroscopy and would place calculated line positions considerably closer to typical experimental accuracy. Such an accuracy has been recently achieved for the H_3^+ molecular ion for line positions up to 17 000 cm ^{-1}. However, since H_3^+ is a two-electron system, the electronic structure methods used in this study are not applicable to larger molecules. A major breakthrough was reported in ref., where an accuracy of 0.10 cm^{-1} was achieved ab initio for seven water isotopologues. Calculated vibrational and rotational energy levels up to 15 000 cm^{-1} and J=25 resulted in a standard deviation of 0.08 cm^{-1} with respect to accurate reference data. As far as line intensities are concerned, we have already achieved for water a typical accuracy of 1% which supersedes average experimental accuracy. Our results are being actively extended along two major directions. First, there are clear indications that our results for water can be improved to an accuracy of the order of 0.01 cm^{-1} by further, detailed ab initio studies. Such level of accuracy would already be competitive with experimental results in some situations. A second, major, direction of study is the extension of such a 0.1 cm^{-1} accuracy to molecules containg more electrons or more than one non-hydrogen atom, or both. As examples of such developments we will present new results for CO, HCN and H_2S, as well as preliminary results for NH_3 and CH_4. O.L. Polyansky, A. Alijah, N.F. Zobov, I.I. Mizus, R. Ovsyannikov, J. Tennyson, L. Lodi, T. Szidarovszky and A.G. Csaszar, Phil. Trans. Royal Soc. London A, {370}, 5014-5027 (2012). O.L. Polyansky, R.I. Ovsyannikov, A.A. Kyuberis, L. Lodi, J. Tennyson and N.F. Zobov, J. Phys. Chem. A, (in press). L. Lodi, J. Tennyson and O.L. Polyansky, J. Chem. Phys. {135}, 034113 (2011).

  7. 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

  8. 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. PMID:27500640

  9. CIRCUIT2. 4. 2-Stage Classification Modeling

    SciTech Connect

    Wilde, R.A. ); Anderson, J.V. )

    1987-12-01

    CIRCUIT2.4 is used to design optimum two-stage classification configurations and operating conditions for energy conservation. It permits simulation of five basic grinding-classification circuits, including one single-stage and four two-stage classification arrangements. Hydrocyclones, spiral classifiers, and sieve band screens can be simulated, and the user may choose the combination of devices for the flowsheet simulation. In addition, the user may select from four classification modeling methods to achieve the goals of a simulation project using the most familiar concepts. Circuit performance is modeled based on classification parameters or equipment operating conditions. A modular approach was taken in designing the program, which allows future addition of other models with relatively minor changes.

  10. Improved algorithms for the classification of rough rice using a bionic electronic nose based on PCA and the Wilks distribution.

    PubMed

    Xu, Sai; Zhou, Zhiyan; Lu, Huazhong; Luo, Xiwen; Lan, Yubin

    2014-03-19

    Principal Component Analysis (PCA) is one of the main methods used for electronic nose pattern recognition. However, poor classification performance is common in classification and recognition when using regular PCA. This paper aims to improve the classification performance of regular PCA based on the existing Wilks Λ-statistic (i.e., combined PCA with the Wilks distribution). The improved algorithms, which combine regular PCA with the Wilks Λ-statistic, were developed after analysing the functionality and defects of PCA. Verification tests were conducted using a PEN3 electronic nose. The collected samples consisted of the volatiles of six varieties of rough rice (Zhongxiang1, Xiangwan13, Yaopingxiang, WufengyouT025, Pin 36, and Youyou122), grown in same area and season. The first two principal components used as analysis vectors cannot perform the rough rice varieties classification task based on a regular PCA. Using the improved algorithms, which combine the regular PCA with the Wilks Λ-statistic, many different principal components were selected as analysis vectors. The set of data points of the Mahalanobis distance between each of the varieties of rough rice was selected to estimate the performance of the classification. The result illustrates that the rough rice varieties classification task is achieved well using the improved algorithm. A Probabilistic Neural Networks (PNN) was also established to test the effectiveness of the improved algorithms. The first two principal components (namely PC1 and PC2) and the first and fifth principal component (namely PC1 and PC5) were selected as the inputs of PNN for the classification of the six rough rice varieties. The results indicate that the classification accuracy based on the improved algorithm was improved by 6.67% compared to the results of the regular method. These results prove the effectiveness of using the Wilks Λ-statistic to improve the classification accuracy of the regular PCA approach. The results

  11. Increasing Accuracy in Computed Inviscid Boundary Conditions

    NASA Technical Reports Server (NTRS)

    Dyson, Roger

    2004-01-01

    A technique has been devised to increase the accuracy of computational simulations of flows of inviscid fluids by increasing the accuracy with which surface boundary conditions are represented. This technique is expected to be especially beneficial for computational aeroacoustics, wherein it enables proper accounting, not only for acoustic waves, but also for vorticity and entropy waves, at surfaces. Heretofore, inviscid nonlinear surface boundary conditions have been limited to third-order accuracy in time for stationary surfaces and to first-order accuracy in time for moving surfaces. For steady-state calculations, it may be possible to achieve higher accuracy in space, but high accuracy in time is needed for efficient simulation of multiscale unsteady flow phenomena. The present technique is the first surface treatment that provides the needed high accuracy through proper accounting of higher-order time derivatives. The present technique is founded on a method known in art as the Hermitian modified solution approximation (MESA) scheme. This is because high time accuracy at a surface depends upon, among other things, correction of the spatial cross-derivatives of flow variables, and many of these cross-derivatives are included explicitly on the computational grid in the MESA scheme. (Alternatively, a related method other than the MESA scheme could be used, as long as the method involves consistent application of the effects of the cross-derivatives.) While the mathematical derivation of the present technique is too lengthy and complex to fit within the space available for this article, the technique itself can be characterized in relatively simple terms: The technique involves correction of surface-normal spatial pressure derivatives at a boundary surface to satisfy the governing equations and the boundary conditions and thereby achieve arbitrarily high orders of time accuracy in special cases. The boundary conditions can now include a potentially infinite number

  12. Toward automated classification of consumers' cancer-related questions with a new taxonomy of expected answer types.

    PubMed

    McRoy, Susan; Jones, Sean; Kurmally, Adam

    2016-09-01

    This article examines methods for automated question classification applied to cancer-related questions that people have asked on the web. This work is part of a broader effort to provide automated question answering for health education. We created a new corpus of consumer-health questions related to cancer and a new taxonomy for those questions. We then compared the effectiveness of different statistical methods for developing classifiers, including weighted classification and resampling. Basic methods for building classifiers were limited by the high variability in the natural distribution of questions and typical refinement approaches of feature selection and merging categories achieved only small improvements to classifier accuracy. Best performance was achieved using weighted classification and resampling methods, the latter yielding an accuracy of F1 = 0.963. Thus, it would appear that statistical classifiers can be trained on natural data, but only if natural distributions of classes are smoothed. Such classifiers would be useful for automated question answering, for enriching web-based content, or assisting clinical professionals to answer questions.

  13. Accuracy assessment of NLCD 2006 land cover and impervious surface

    USGS Publications Warehouse

    Wickham, James D.; Stehman, Stephen V.; Gass, Leila; Dewitz, Jon; Fry, Joyce A.; Wade, Timothy G.

    2013-01-01

    Release of NLCD 2006 provides the first wall-to-wall land-cover change database for the conterminous United States from Landsat Thematic Mapper (TM) data. Accuracy assessment of NLCD 2006 focused on four primary products: 2001 land cover, 2006 land cover, land-cover change between 2001 and 2006, and impervious surface change between 2001 and 2006. The accuracy assessment was conducted by selecting a stratified random sample of pixels with the reference classification interpreted from multi-temporal high resolution digital imagery. The NLCD Level II (16 classes) overall accuracies for the 2001 and 2006 land cover were 79% and 78%, respectively, with Level II user's accuracies exceeding 80% for water, high density urban, all upland forest classes, shrubland, and cropland for both dates. Level I (8 classes) accuracies were 85% for NLCD 2001 and 84% for NLCD 2006. The high overall and user's accuracies for the individual dates translated into high user's accuracies for the 2001–2006 change reporting themes water gain and loss, forest loss, urban gain, and the no-change reporting themes for water, urban, forest, and agriculture. The main factor limiting higher accuracies for the change reporting themes appeared to be difficulty in distinguishing the context of grass. We discuss the need for more research on land-cover change accuracy assessment.

  14. [Accuracy of winter wheat identification based on multi-temporal CBERS-02 images].

    PubMed

    Qi, La; Zhao, Chun-Jiang; Li, Cun-Jun; Liu, Liang-Yun; Tan, Chang-Wei; Huang, Wen-Jiang

    2008-10-01

    Chinese-Brazil Earth Resources Satellite No. 2 (CBERS-02) has good spatial resolution and abundant spectral information, and a strong ability in detecting vegetation. Based on five CBERS-02 images in winter wheat growth season, the spectral distance between winter wheat and other ground targets was calculated, and then, winter wheat was classified from each individual image or their combinations by using supervised classification. The train and validation samples were derived from high resolution Aerial Images and SPOT5 images. The accuracies and analyses were evaluated for CBERS-02 images at early growth stages, and the results were compared to those of TM images acquired in the same phenological calendars. The results showed that temporal information was the main factor affecting the classification accuracy in winter wheat, but the characteristics of different sensors could affect the classification accuracy. The multi-temporal images could improve the classification accuracy, compared with the results derived from signal stage, with the producer accuracy of optimum periods combination improved 20.0% and user accuracy improved 7.83%. Compared with TM sensor, the classification accuracy from CBERS-02 was a little lower.

  15. The Attribute Accuracy Assessment of Land Cover Data in the National Geographic Conditions Survey

    NASA Astrophysics Data System (ADS)

    Ji, X.; Niu, X.

    2014-04-01

    With the widespread national survey of geographic conditions, object-based data has already became the most common data organization pattern in the area of land cover research. Assessing the accuracy of object-based land cover data is related to lots of processes of data production, such like the efficiency of inside production and the quality of final land cover data. Therefore,there are a great deal of requirements of accuracy assessment of object-based classification map. Traditional approaches for accuracy assessment in surveying and mapping are not aimed at land cover data. It is necessary to employ the accuracy assessment in imagery classification. However traditional pixel-based accuracy assessing methods are inadequate for the requirements. The measures we improved are based on error matrix and using objects as sample units, because the pixel sample units are not suitable for assessing the accuracy of object-based classification result. Compared to pixel samples, we realize that the uniformity of object samples has changed. In order to make the indexes generating from error matrix reliable, we using the areas of object samples as the weight to establish the error matrix of object-based image classification map. We compare the result of two error matrixes setting up by the number of object samples and the sum of area of object samples. The error matrix using the sum of area of object sample is proved to be an intuitive, useful technique for reflecting the actual accuracy of object-based imagery classification result.

  16. Classification of Urban Feature from Unmanned Aerial Vehicle Images Using Gasvm Integration and Multi-Scale Segmentation

    NASA Astrophysics Data System (ADS)

    Modiri, M.; Salehabadi, A.; Mohebbi, M.; Hashemi, A. M.; Masumi, M.

    2015-12-01

    The use of UAV in the application of photogrammetry to obtain cover images and achieve the main objectives of the photogrammetric mapping has been a boom in the region. The images taken from REGGIOLO region in the province of, Italy Reggio -Emilia by UAV with non-metric camera Canon Ixus and with an average height of 139.42 meters were used to classify urban feature. Using the software provided SURE and cover images of the study area, to produce dense point cloud, DSM and Artvqvtv spatial resolution of 10 cm was prepared. DTM area using Adaptive TIN filtering algorithm was developed. NDSM area was prepared with using the difference between DSM and DTM and a separate features in the image stack. In order to extract features, using simultaneous occurrence matrix features mean, variance, homogeneity, contrast, dissimilarity, entropy, second moment, and correlation for each of the RGB band image was used Orthophoto area. Classes used to classify urban problems, including buildings, trees and tall vegetation, grass and vegetation short, paved road and is impervious surfaces. Class consists of impervious surfaces such as pavement conditions, the cement, the car, the roof is stored. In order to pixel-based classification and selection of optimal features of classification was GASVM pixel basis. In order to achieve the classification results with higher accuracy and spectral composition informations, texture, and shape conceptual image featureOrthophoto area was fencing. The segmentation of multi-scale segmentation method was used.it belonged class. Search results using the proposed classification of urban feature, suggests the suitability of this method of classification complications UAV is a city using images. The overall accuracy and kappa coefficient method proposed in this study, respectively, 47/93% and 84/91% was.

  17. A contour-based shape descriptor for biomedical image classification and retrieval

    NASA Astrophysics Data System (ADS)

    You, Daekeun; Antani, Sameer; Demner-Fushman, Dina; Thoma, George R.

    2013-12-01

    Contours, object blobs, and specific feature points are utilized to represent object shapes and extract shape descriptors that can then be used for object detection or image classification. In this research we develop a shape descriptor for biomedical image type (or, modality) classification. We adapt a feature extraction method used in optical character recognition (OCR) for character shape representation, and apply various image preprocessing methods to successfully adapt the method to our application. The proposed shape descriptor is applied to radiology images (e.g., MRI, CT, ultrasound, X-ray, etc.) to assess its usefulness for modality classification. In our experiment we compare our method with other visual descriptors such as CEDD, CLD, Tamura, and PHOG that extract color, texture, or shape information from images. The proposed method achieved the highest classification accuracy of 74.1% among all other individual descriptors in the test, and when combined with CSD (color structure descriptor) showed better performance (78.9%) than using the shape descriptor alone.

  18. 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.

  19. Spectral and Image Integrated Analysis of Hyperspectral Data for Waxy Corn Seed Variety Classification

    PubMed Central

    Yang, Xiaoling; Hong, Hanmei; You, Zhaohong; Cheng, Fang

    2015-01-01

    The purity of waxy corn seed is a very important index of seed quality. A novel procedure for the classification of corn seed varieties was developed based on the combined spectral, morphological, and texture features extracted from visible and near-infrared (VIS/NIR) hyperspectral images. For the purpose of exploration and comparison, images of both sides of corn kernels (150 kernels of each variety) were captured and analyzed. The raw spectra were preprocessed with Savitzky-Golay (SG) smoothing and derivation. To reduce the dimension of spectral data, the spectral feature vectors were constructed using the successive projections algorithm (SPA). Five morphological features (area, circularity, aspect ratio, roundness, and solidity) and eight texture features (energy, contrast, correlation, entropy, and their standard deviations) were extracted as appearance character from every corn kernel. Support vector machines (SVM) and a partial least squares–discriminant analysis (PLS-DA) model were employed to build the classification models for seed varieties classification based on different groups of features. The results demonstrate that combining spectral and appearance characteristic could obtain better classification results. The recognition accuracy achieved in the SVM model (98.2% and 96.3% for germ side and endosperm side, respectively) was more satisfactory than in the PLS-DA model. This procedure has the potential for use as a new method for seed purity testing. PMID:26140347

  20. 3D texture analysis for classification of second harmonic generation images of human ovarian cancer

    PubMed Central

    Wen, Bruce; Campbell, Kirby R.; Tilbury, Karissa; Nadiarnykh, Oleg; Brewer, Molly A.; Patankar, Manish; Singh, Vikas; Eliceiri, Kevin. W.; Campagnola, Paul J.

    2016-01-01

    Remodeling of the collagen architecture in the extracellular matrix (ECM) has been implicated in ovarian cancer. To quantify these alterations we implemented a form of 3D texture analysis to delineate the fibrillar morphology observed in 3D Second Harmonic Generation (SHG) microscopy image data of normal (1) and high risk (2) ovarian stroma, benign ovarian tumors (3), low grade (4) and high grade (5) serous tumors, and endometrioid tumors (6). We developed a tailored set of 3D filters which extract textural features in the 3D image sets to build (or learn) statistical models of each tissue class. By applying k-nearest neighbor classification using these learned models, we achieved 83–91% accuracies for the six classes. The 3D method outperformed the analogous 2D classification on the same tissues, where we suggest this is due the increased information content. This classification based on ECM structural changes will complement conventional classification based on genetic profiles and can serve as an additional biomarker. Moreover, the texture analysis algorithm is quite general, as it does not rely on single morphological metrics such as fiber alignment, length, and width but their combined convolution with a customizable basis set. PMID:27767180

  1. Sparse coding based dense feature representation model for hyperspectral image classification

    NASA Astrophysics Data System (ADS)

    Oguslu, Ender; Zhou, Guoqing; Zheng, Zezhong; Iftekharuddin, Khan; Li, Jiang

    2015-11-01

    We present a sparse coding based dense feature representation model (a preliminary version of the paper was presented at the SPIE Remote Sensing Conference, Dresden, Germany, 2013) for hyperspectral image (HSI) classification. The proposed method learns a new representation for each pixel in HSI through the following four steps: sub-band construction, dictionary learning, encoding, and feature selection. The new representation usually has a very high dimensionality requiring a large amount of computational resources. We applied the l1/lq regularized multiclass logistic regression technique to reduce the size of the new representation. We integrated the method with a linear support vector machine (SVM) and a composite kernels SVM (CKSVM) to discriminate different types of land cover. We evaluated the proposed algorithm on three well-known HSI datasets and compared our method to four recently developed classification methods: SVM, CKSVM, simultaneous orthogonal matching pursuit, and image fusion and recursive filtering. Experimental results show that the proposed method can achieve better overall and average classification accuracies with a much more compact representation leading to more efficient sparse models for HSI classification.

  2. Classification of Fruits Using Computer Vision and a Multiclass Support Vector Machine

    PubMed Central

    Zhang, Yudong; Wu, Lenan

    2012-01-01

    Automatic classification of fruits via computer vision is still a complicated task due to the various properties of numerous types of fruits. We propose a novel classification method based on a multi-class kernel support vector machine (kSVM) with the desirable goal of accurate and fast classification of fruits. First, fruit images were acquired by a digital camera, and then the background of each image was removed by a split-and-merge algorithm; Second, the color histogram, texture and shape features of each fruit image were extracted to compose a feature space; Third, principal component analysis (PCA) was used to reduce the dimensions of feature space; Finally, three kinds of multi-class SVMs were constructed, i.e., Winner-Takes-All SVM, Max-Wins-Voting SVM, and Directed Acyclic Graph SVM. Meanwhile, three kinds of kernels were chosen, i.e., linear kernel, Homogeneous Polynomial kernel, and Gaussian Radial Basis kernel; finally, the SVMs were trained using 5-fold stratified cross validation with the reduced feature vectors as input. The experimental results demonstrated that the Max-Wins-Voting SVM with Gaussian Radial Basis kernel achieves the best classification accuracy of 88.2%. For computation time, the Directed Acyclic Graph SVMs performs swiftest. PMID:23112727

  3. A class-oriented model for hyperspectral image classification through hierarchy-tree-based selection

    NASA Astrophysics Data System (ADS)

    Tang, Zhongqi; Fu, Guangyuan; Zhao, XiaoLin; Chen, Jin; Zhang, Li

    2016-03-01

    With the development of hyperspectral sensors over the last few decades, hyperspectral images (HSIs) face new challenges in the field of data analysis. Due to those high-dimensional data, the most challenging issue is to select an effective yet minimal subset from a mass of bands. This paper proposes a class-oriented model to solve the task of classification by incorporating spectral prior of the target, since different targets have different characteristics in spectral correlation. This model operates feature selection after a partition of hyperspectral data into groups along the spectral dimension. In the process of spectral partition, we group the raw data into several subsets by a hierarchy tree structure. In each group, band selection is performed via a recursive support vector machine (R-SVM) learning, which reduces the computational cost as well as preserves the accuracy of classification. To ensure the robustness of the result, we also present a weight-voting strategy for result merging, in which the spectral independency and the classification effectivity are both considered. Extensive experiments show that our model achieves better performance than the existing methods in task-dependent classifications, such as target detection and identification.

  4. Machine Learning Algorithms for Automatic Classification of Marmoset Vocalizations

    PubMed Central

    Ribeiro, Sidarta; Pereira, Danillo R.; Papa, João P.; de Albuquerque, Victor Hugo C.

    2016-01-01

    Automatic classification of vocalization type could potentially become a useful tool for acoustic the monitoring of captive colonies of highly vocal primates. However, for classification to be useful in practice, a reliable algorithm that can be successfully trained on small datasets is necessary. In this work, we consider seven different classification algorithms with the goal of finding a robust classifier that can be successfully trained on small datasets. We found good classification performance (accuracy > 0.83 and F1-score > 0.84) using the Optimum Path Forest classifier. Dataset and algorithms are made publicly available. PMID:27654941

  5. Multiple Hypotheses Image Segmentation and Classification With Application to Dietary Assessment

    PubMed Central

    Zhu, Fengqing; Bosch, Marc; Khanna, Nitin; Boushey, Carol J.; Delp, Edward J.

    2016-01-01

    We propose a method for dietary assessment to automatically identify and locate food in a variety of images captured during controlled and natural eating events. Two concepts are combined to achieve this: a set of segmented objects can be partitioned into perceptually similar object classes based on global and local features; and perceptually similar object classes can be used to assess the accuracy of image segmentation. These ideas are implemented by generating multiple segmentations of an image to select stable segmentations based on the classifier’s confidence score assigned to each segmented image region. Automatic segmented regions are classified using a multichannel feature classification system. For each segmented region, multiple feature spaces are formed. Feature vectors in each of the feature spaces are individually classified. The final decision is obtained by combining class decisions from individual feature spaces using decision rules. We show improved accuracy of segmenting food images with classifier feedback. PMID:25561457

  6. A neuro-fuzzy approach in the classification of students' academic performance.

    PubMed

    Do, Quang Hung; Chen, Jeng-Fung

    2013-01-01

    Classifying the student academic performance with high accuracy facilitates admission decisions and enhances educational services at educational institutions. The purpose of this paper is to present a neuro-fuzzy approach for classifying students into different groups. The neuro-fuzzy classifier used previous exam results and other related factors as input variables and labeled students based on their expected academic performance. The results showed that the proposed approach achieved a high accuracy. The results were also compared with those obtained from other well-known classification approaches, including support vector machine, Naive Bayes, neural network, and decision tree approaches. The comparative analysis indicated that the neuro-fuzzy approach performed better than the others. It is expected that this work may be used to support student admission procedures and to strengthen the services of educational institutions.

  7. A Neuro-Fuzzy Approach in the Classification of Students' Academic Performance

    PubMed Central

    2013-01-01

    Classifying the student academic performance with high accuracy facilitates admission decisions and enhances educational services at educational institutions. The purpose of this paper is to present a neuro-fuzzy approach for classifying students into different groups. The neuro-fuzzy classifier used previous exam results and other related factors as input variables and labeled students based on their expected academic performance. The results showed that the proposed approach achieved a high accuracy. The results were also compared with those obtained from other well-known classification approaches, including support vector machine, Naive Bayes, neural network, and decision tree approaches. The comparative analysis indicated that the neuro-fuzzy approach performed better than the others. It is expected that this work may be used to support student admission procedures and to strengthen the services of educational institutions. PMID:24302928

  8. Aircraft Operations Classification System

    NASA Technical Reports Server (NTRS)

    Harlow, Charles; Zhu, Weihong

    2001-01-01

    Accurate data is important in the aviation planning process. In this project we consider systems for measuring aircraft activity at airports. This would include determining the type of aircraft such as jet, helicopter, single engine, and multiengine propeller. Some of the issues involved in deploying technologies for monitoring aircraft operations are cost, reliability, and accuracy. In addition, the system must be field portable and acceptable at airports. A comparison of technologies was conducted and it was decided that an aircraft monitoring system should be based upon acoustic technology. A multimedia relational database was established for the study. The information contained in the database consists of airport information, runway information, acoustic records, photographic records, a description of the event (takeoff, landing), aircraft type, and environmental information. We extracted features from the time signal and the frequency content of the signal. A multi-layer feed-forward neural network was chosen as the classifier. Training and testing results were obtained. We were able to obtain classification results of over 90 percent for training and testing for takeoff events.

  9. Multimodal Classification of Mild Cognitive Impairment Based on Partial Least Squares.

    PubMed

    Wang, Pingyue; Chen, Kewei; Yao, Li; Hu, Bin; Wu, Xia; Zhang, Jiacai; Ye, Qing; Guo, Xiaojuan

    2016-08-10

    In recent years, increasing attention has been given to the identification of the conversion of mild cognitive impairment (MCI) to Alzheimer's disease (AD). Brain neuroimaging techniques have been widely used to support the classification or prediction of MCI. The present study combined magnetic resonance imaging (MRI), 18F-fluorodeoxyglucose PET (FDG-PET), and 18F-florbetapir PET (florbetapir-PET) to discriminate MCI converters (MCI-c, individuals with MCI who convert to AD) from MCI non-converters (MCI-nc, individuals with MCI who have not converted to AD in the follow-up period) based on the partial least squares (PLS) method. Two types of PLS models (informed PLS and agnostic PLS) were built based on 64 MCI-c and 65 MCI-nc from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. The results showed that the three-modality informed PLS model achieved better classification accuracy of 81.40%, sensitivity of 79.69%, and specificity of 83.08% compared with the single-modality model, and the three-modality agnostic PLS model also achieved better classification compared with the two-modality model. Moreover, combining the three modalities with clinical test score (ADAS-cog), the agnostic PLS model (independent data: florbetapir-PET; dependent data: FDG-PET and MRI) achieved optimal accuracy of 86.05%, sensitivity of 81.25%, and specificity of 90.77%. In addition, the comparison of PLS, support vector machine (SVM), and random forest (RF) showed greater diagnostic power of PLS. These results suggested that our multimodal PLS model has the potential to discriminate MCI-c from the MCI-nc and may therefore be helpful in the early diagnosis of AD.

  10. Multimodal Classification of Mild Cognitive Impairment Based on Partial Least Squares.

    PubMed

    Wang, Pingyue; Chen, Kewei; Yao, Li; Hu, Bin; Wu, Xia; Zhang, Jiacai; Ye, Qing; Guo, Xiaojuan

    2016-08-10

    In recent years, increasing attention has been given to the identification of the conversion of mild cognitive impairment (MCI) to Alzheimer's disease (AD). Brain neuroimaging techniques have been widely used to support the classification or prediction of MCI. The present study combined magnetic resonance imaging (MRI), 18F-fluorodeoxyglucose PET (FDG-PET), and 18F-florbetapir PET (florbetapir-PET) to discriminate MCI converters (MCI-c, individuals with MCI who convert to AD) from MCI non-converters (MCI-nc, individuals with MCI who have not converted to AD in the follow-up period) based on the partial least squares (PLS) method. Two types of PLS models (informed PLS and agnostic PLS) were built based on 64 MCI-c and 65 MCI-nc from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. The results showed that the three-modality informed PLS model achieved better classification accuracy of 81.40%, sensitivity of 79.69%, and specificity of 83.08% compared with the single-modality model, and the three-modality agnostic PLS model also achieved better classification compared with the two-modality model. Moreover, combining the three modalities with clinical test score (ADAS-cog), the agnostic PLS model (independent data: florbetapir-PET; dependent data: FDG-PET and MRI) achieved optimal accuracy of 86.05%, sensitivity of 81.25%, and specificity of 90.77%. In addition, the comparison of PLS, support vector machine (SVM), and random forest (RF) showed greater diagnostic power of PLS. These results suggested that our multimodal PLS model has the potential to discriminate MCI-c from the MCI-nc and may therefore be helpful in the early diagnosis of AD. PMID:27567818

  11. a Dimension Reduction-Based Method for Classification of Hyperspectral and LIDAR Data

    NASA Astrophysics Data System (ADS)

    Abbasi, B.; Arefi, H.; Bigdeli, B.

    2015-12-01

    The existence of various natural objects such as grass, trees, and rivers along with artificial manmade features such as buildings and roads, make it difficult to classify ground objects. Consequently using single data or simple classification approach cannot improve classification results in object identification. Also, using of a variety of data from different sensors; increase the accuracy of spatial and spectral information. In this paper, we proposed a classification algorithm on joint use of hyperspectral and Lidar (Light Detection and Ranging) data based on dimension reduction. First, some feature extraction techniques are applied to achieve more information from Lidar and hyperspectral data. Also Principal component analysis (PCA) and Minimum Noise Fraction (MNF) have been utilized to reduce the dimension of spectral features. The number of 30 features containing the most information of the hyperspectral images is considered for both PCA and MNF. In addition, Normalized Difference Vegetation Index (NDVI) has been measured to highlight the vegetation. Furthermore, the extracted features from Lidar data calculated based on relation between every pixel of data and surrounding pixels in local neighbourhood windows. The extracted features are based on the Grey Level Co-occurrence Matrix (GLCM) matrix. In second step, classification is operated in all features which obtained by MNF, PCA, NDVI and GLCM and trained by class samples. After this step, two classification maps are obtained by SVM classifier with MNF+NDVI+GLCM features and PCA+NDVI+GLCM features, respectively. Finally, the classified images are fused together to create final classification map by decision fusion based majority voting strategy.

  12. Computer-aided classification of liver tumors in 3D ultrasound images with combined deformable model segmentation and support vector machine

    NASA Astrophysics Data System (ADS)

    Lee, Myungeun; Kim, Jong Hyo; Park, Moon Ho; Kim, Ye-Hoon; Seong, Yeong Kyeong; Cho, Baek Hwan; Woo, Kyoung-Gu

    2014-03-01

    In this study, we propose a computer-aided classification scheme of liver tumor in 3D ultrasound by using a combination of deformable model segmentation and support vector machine. For segmentation of tumors in 3D ultrasound images, a novel segmentation model was used which combined edge, region, and contour smoothness energies. Then four features were extracted from the segmented tumor including tumor edge, roundness, contrast, and internal texture. We used a support vector machine for the classification of features. The performance of the developed method was evaluated with a dataset of 79 cases including 20 cysts, 20 hemangiomas, and 39 hepatocellular carcinomas, as determined by the radiologist's visual scoring. Evaluation of the results showed that our proposed method produced tumor boundaries that were equal to or better than acceptable in 89.8% of cases, and achieved 93.7% accuracy in classification of cyst and hemangioma.

  13. Classification systems for natural resource management

    USGS Publications Warehouse

    Kleckner, Richard L.

    1981-01-01

    Resource managers employ various types of resource classification systems in their management activities such as inventory, mapping, and data analysis. Classification is the ordering or arranging of objects into groups or sets on the basis of their relationships, and as such, provide the resource managers with a structure for organizing their needed information. In addition of conforming to certain logical principles, resource classifications should be flexible, widely applicable to a variety of environmental conditions, and useable with minimal training. The process of classification may be approached from the bottom up (aggregation) or the top down (subdivision) or a combination of both, depending on the purpose of the classification. Most resource classification systems in use today focus on a single resource and are used for a single, limited purpose. However, resource managers now must employ the concept of multiple use in their management activities. What they need is an integrated, ecologically based approach to resource classification which would fulfill multiple-use mandates. In an effort to achieve resource-data compatibility and data sharing among Federal agencies, and interagency agreement has been signed by five Federal agencies to coordinate and cooperate in the area of resource classification and inventory.

  14. Classification Shell Game.

    ERIC Educational Resources Information Center

    Etzold, Carol

    1983-01-01

    Discusses shell classification exercises. Through keying students advanced from the "I know what a shell looks like" stage to become involved in the classification process: observing, labeling, making decisions about categories, and identifying marine animals. (Author/JN)

  15. Prediction of Rate Constants for Catalytic Reactions with Chemical Accuracy.

    PubMed

    Catlow, C Richard A

    2016-08-01

    Ex machina: A computational method for predicting rate constants for reactions within microporous zeolite catalysts with chemical accuracy has recently been reported. A key feature of this method is a stepwise QM/MM approach that allows accuracy to be achieved while using realistic models with accessible computer resources.

  16. Accuracy of migrant landbird habitat maps produced from LANDSAT TM data: Two case studies in southern Belize

    USGS Publications Warehouse

    Spruce, J.P.; Sader, S.; Robbins, C.S.; Dowell, B.A.; Wilson, Marcia H.; Sader, Steven A.

    1995-01-01

    The study investigated the utility of Landsat TM data applied to produce geo-referenced habitat maps for two study areas (Toledo and Stann Creek). Locational and non-site-specific map accuracy was evaluated by stratified random sampling and statistical analysis of satellite classification (SCR) versus air photo interpretation results (PIR) for the overall classification and individual classes. The effect of classification scheme specificity on map accuracy was also assessed. A decision criteria was developed for the minimum acceptable level of map performance (i.e., classification accuracy and scheme specificity). A satellite map was deemed acceptable if it has a useful degree of classification specificity, plus either an adequate overall locational agreement (< 70%) and/or non-site specific agreement (Chi Square goodness of fit test results indicating insufficient evidence to reject the null hypothesis that the overall classification distribution for the SCR and PIR are equal). For the most detailed revised classification, overall locational accuracy ranges from 52% (5 classes) for the Toledo to 63% (9 classes) for the Stann Creek. For the least detailed revised classification, overall locational accuracy ranges from 91% (2 classes) for Toledo to 86% (5 classes) for Stann Creek. Considering both location and non-site-specific accuracy results, the most detailed yet insufficient accurate classification for both sites includes low/medium/tall broadleaf forest, broadleaf forest scrub and herb-dominated openings. For these classifications, the overall locational accuracy is 72% for Toledo (4 classes) and 75% for Stann Creek (7 classes). This level of classification detail is suitable for aiding many analyses of migrant landbird habitat use.

  17. 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.

  18. Automatic classification framework for ventricular septal defects: a pilot study on high-throughput mouse embryo cardiac phenotyping.

    PubMed

    Xie, Zhongliu; Liang, Xi; Guo, Liucheng; Kitamoto, Asanobu; Tamura, Masaru; Shiroishi, Toshihiko; Gillies, Duncan

    2015-10-01

    Intensive international efforts are underway toward phenotyping the entire mouse genome by modifying all its [Formula: see text] genes one-by-one for comparative studies. A workload of this scale has triggered numerous studies harnessing image informatics for the identification of morphological defects. However, existing work in this line primarily rests on abnormality detection via structural volumetrics between wild-type and gene-modified mice, which generally fails when the pathology involves no severe volume changes, such as ventricular septal defects (VSDs) in the heart. Furthermore, in embryo cardiac phenotyping, the lack of relevant work in embryonic heart segmentation, the limited availability of public atlases, and the general requirement of manual labor for the actual phenotype classification after abnormality detection, along with other limitations, have collectively restricted existing practices from meeting the high-throughput demands. This study proposes, to the best of our knowledge, the first fully automatic VSD classification framework in mouse embryo imaging. Our approach leverages a combination of atlas-based segmentation and snake evolution techniques to derive the segmentation of heart ventricles, where VSD classification is achieved by checking whether the left and right ventricles border or overlap with each other. A pilot study has validated our approach at a proof-of-concept level and achieved a classification accuracy of 100% through a series of empirical experiments on a database of 15 images. PMID:26835488

  19. Smartphone application for classification of motor impairment severity in Parkinson's disease.

    PubMed

    Printy, Blake P; Renken, Lindsey M; Herrmann, John P; Lee, Isac; Johnson, Bryant; Knight, Emily; Varga, Georgeta; Whitmer, Diane

    2014-01-01

    Advanced hardware components embedded in modern smartphones have the potential to serve as widely available medical diagnostic devices, particularly when used in conjunction with custom software and tested algorithms. The goal of the present pilot study was to develop a smartphone application that could quantify the severity of Parkinson's disease (PD) motor symptoms, and in particular, bradykinesia. We developed an iPhone application that collected kinematic data from a small cohort of PD patients during guided movement tasks and extracted quantitative features using signal processing techniques. These features were used in a classification model trained to differentiate between overall motor impairment of greater and lesser severity using standard clinical scores provided by a trained neurologist. Using a support vector machine classifier, a classification accuracy of 0.945 was achieved under 6-fold cross validation, and several features were shown to be highly discriminatory between more severe and less severe motor impairment by area under the receiver operating characteristic curve (AUC > 0.85). Accurate classification for discriminating between more severe and less severe bradykinesia was not achieved with these methods. We discuss future directions of this work and suggest that this platform is a first step toward development of a smartphone application that has the potential to provide clinicians with a method for monitoring patients between clinical appointments.

  20. The effect of storage temperature on the accuracy of a cow-side test for ketosis.

    PubMed

    Hubbard, Jennifer; LeBlanc, Stephen; Duffield, Todd; Bagg, Randal; Dubuc, Jocelyn

    2010-05-01

    The objective of this study was to assess the effect of storage conditions on the accuracy of a milk test strip for ketosis. Storage at 21 degrees C for up to 18 wk had little effect on accuracy for diagnosis and classification of subclinical ketosis.

  1. Refining Time-Activity Classification of Human Subjects Using the Global Positioning System

    PubMed Central

    Hu, Maogui; Li, Wei; Li, Lianfa; Houston, Douglas; Wu, Jun

    2016-01-01

    Background Detailed spatial location information is important in accurately estimating personal exposure to air pollution. Global Position System (GPS) has been widely used in tracking personal paths and activities. Previous researchers have developed time-activity classification models based on GPS data, most of them were developed for specific regions. An adaptive model for time-location classification can be widely applied to air pollution studies that use GPS to track individual level time-activity patterns. Methods Time-activity data were collected for seven days using GPS loggers and accelerometers from thirteen adult participants from Southern California under free living conditions. We developed an automated model based on random forests to classify major time-activity patterns (i.e. indoor, outdoor-static, outdoor-walking, and in-vehicle travel). Sensitivity analysis was conducted to examine the contribution of the accelerometer data and the supplemental spatial data (i.e. roadway and tax parcel data) to the accuracy of time-activity classification. Our model was evaluated using both leave-one-fold-out and leave-one-subject-out methods. Results Maximum speeds in averaging time intervals of 7 and 5 minutes, and distance to primary highways with limited access were found to be the three most important variables in the classification model. Leave-one-fold-out cross-validation showed an overall accuracy of 99.71%. Sensitivities varied from 84.62% (outdoor walking) to 99.90% (indoor). Specificities varied from 96.33% (indoor) to 99.98% (outdoor static). The exclusion of accelerometer and ambient light sensor variables caused a slight loss in sensitivity for outdoor walking, but little loss in overall accuracy. However, leave-one-subject-out cross-validation showed considerable loss in sensitivity for outdoor static and outdoor walking conditions. Conclusions The random forests classification model can achieve high accuracy for the four major time

  2. Object oriented classification of high resolution data for inventory of horticultural crops

    NASA Astrophysics Data System (ADS)

    Hebbar, R.; Ravishankar, H. M.; Trivedi, S.; Subramoniam, S. R.; Uday, R.; Dadhwal, V. K.

    2014-11-01

    High resolution satellite images are associated with large variance and thus, per pixel classifiers often result in poor accuracy especially in delineation of horticultural crops. In this context, object oriented techniques are powerful and promising methods for classification. In the present study, a semi-automatic object oriented feature extraction model has been used for delineation of horticultural fruit and plantation crops using Erdas Objective Imagine. Multi-resolution data from Resourcesat LISS-IV and Cartosat-1 have been used as source data in the feature extraction model. Spectral and textural information along with NDVI were used as inputs for generation of Spectral Feature Probability (SFP) layers using sample training pixels. The SFP layers were then converted into raster objects using threshold and clump function resulting in pixel probability layer. A set of raster and vector operators was employed in the subsequent steps for generating thematic layer in the vector format. This semi-automatic feature extraction model was employed for classification of major fruit and plantations crops viz., mango, banana, citrus, coffee and coconut grown under different agro-climatic conditions. In general, the classification accuracy of about 75-80 per cent was achieved for these crops using object based classification alone and the same was further improved using minimal visual editing of misclassified areas. A comparison of on-screen visual interpretation with object oriented approach showed good agreement. It was observed that old and mature plantations were classified more accurately while young and recently planted ones (3 years or less) showed poor classification accuracy due to mixed spectral signature, wider spacing and poor stands of plantations. The results indicated the potential use of object oriented approach for classification of high resolution data for delineation of horticultural fruit and plantation crops. The present methodology is applicable at

  3. Comparison of Classification Methods for Detecting Emotion from Mandarin Speech

    NASA Astrophysics Data System (ADS)

    Pao, Tsang-Long; Chen, Yu-Te; Yeh, Jun-Heng

    It is said that technology comes out from humanity. What is humanity? The very definition of humanity is emotion. Emotion is the basis for all human expression and the underlying theme behind everything that is done, said, thought or imagined. Making computers being able to perceive and respond to human emotion, the human-computer interaction will be more natural. Several classifiers are adopted for automatically assigning an emotion category, such as anger, happiness or sadness, to a speech utterance. These classifiers were designed independently and tested on various emotional speech corpora, making it difficult to compare and evaluate their performance. In this paper, we first compared several popular classification methods and evaluated their performance by applying them to a Mandarin speech corpus consisting of five basic emotions, including anger, happiness, boredom, sadness and neutral. The extracted feature streams contain MFCC, LPCC, and LPC. The experimental results show that the proposed WD-MKNN classifier achieves an accuracy of 81.4% for the 5-class emotion recognition and outperforms other classification techniques, including KNN, MKNN, DW-KNN, LDA, QDA, GMM, HMM, SVM, and BPNN. Then, to verify the advantage of the proposed method, we compared these classifiers by applying them to another Mandarin expressive speech corpus consisting of two emotions. The experimental results still show that the proposed WD-MKNN outperforms others.

  4. Automatic Classification of Specific Melanocytic Lesions Using Artificial Intelligence

    PubMed Central

    Jaworek-Korjakowska, Joanna; Kłeczek, Paweł

    2016-01-01

    Background. Given its propensity to metastasize, and lack of effective therapies for most patients with advanced disease, early detection of melanoma is a clinical imperative. Different computer-aided diagnosis (CAD) systems have been proposed to increase the specificity and sensitivity of melanoma detection. Although such computer programs are developed for different diagnostic algorithms, to the best of our knowledge, a system to classify different melanocytic lesions has not been proposed yet. Method. In this research we present a new approach to the classification of melanocytic lesions. This work is focused not only on categorization of skin lesions as benign or malignant but also on specifying the exact type of a skin lesion including melanoma, Clark nevus, Spitz/Reed nevus, and blue nevus. The proposed automatic algorithm contains the following steps: image enhancement, lesion segmentation, feature extraction, and selection as well as classification. Results. The algorithm has been tested on 300 dermoscopic images and achieved accuracy of 92% indicating that the proposed approach classified most of the melanocytic lesions correctly. Conclusions. A proposed system can not only help to precisely diagnose the type of the skin mole but also decrease the amount of biopsies and reduce the morbidity related to skin lesion excision. PMID:26885520

  5. Clinically-inspired automatic classification of ovarian carcinoma subtypes

    PubMed Central

    BenTaieb, Aïcha; Nosrati, Masoud S; Li-Chang, Hector; Huntsman, David; Hamarneh, Ghassan

    2016-01-01

    Context: It has been shown that ovarian carcinoma subtypes are distinct pathologic entities with differing prognostic and therapeutic implications. Histotyping by pathologists has good reproducibility, but occasional cases are challenging and require immunohistochemistry and subspecialty consultation. Motivated by the need for more accurate and reproducible diagnoses and to facilitate pathologists’ workflow, we propose an automatic framework for ovarian carcinoma classification. Materials and Methods: Our method is inspired by pathologists’ workflow. We analyse imaged tissues at two magnification levels and extract clinically-inspired color, texture, and segmentation-based shape descriptors using image-processing methods. We propose a carefully designed machine learning technique composed of four modules: A dissimilarity matrix, dimensionality reduction, feature selection and a support vector machine classifier to separate the five ovarian carcinoma subtypes using the extracted features. Results: This paper presents the details of our implementation and its validation on a clinically derived dataset of eighty high-resolution histopathology images. The proposed system achieved a multiclass classification accuracy of 95.0% when classifying unseen tissues. Assessment of the classifier's confusion (confusion matrix) between the five different ovarian carcinoma subtypes agrees with clinician's confusion and reflects the difficulty in diagnosing endometrioid and serous carcinomas. Conclusions: Our results from this first study highlight the difficulty of ovarian carcinoma diagnosis which originate from the intrinsic class-imbalance observed among subtypes and suggest that the automatic analysis of ovarian carcinoma subtypes could be valuable to clinician's diagnostic procedure by providing a second opinion. PMID:27563487

  6. Enhancement classification of galaxy images

    NASA Astrophysics Data System (ADS)

    Jenkinson, John

    With the advent of astronomical imaging technology developments, and the increased capacity of digital storage, the production of photographic atlases of the night sky have begun to generate volumes of data which need to be processed autonomously. As part of the Tonantzintla Digital Sky Survey construction, the present work involves software development for the digital image processing of astronomical images, in particular operations that preface feature extraction and classification. Recognition of galaxies in these images is the primary objective of the present work. Many galaxy images have poor resolution or contain faint galaxy features, resulting in the misclassification of galaxies. An enhancement of these images by the method of the Heap transform is proposed, and experimental results are provided which demonstrate the image enhancement to improve the presence of faint galaxy features thereby improving classification accuracy. The feature extraction was performed using morphological features that have been widely used in previous automated galaxy investigations. Principal component analysis was applied to the original and enhanced data sets for a performance comparison between the original and reduced features spaces. Classification was performed by the Support Vector Machine learning algorithm.

  7. Terrain classification maps of Yellowstone National Park

    NASA Technical Reports Server (NTRS)

    Thomson, F. J.; Roller, N. E. G.

    1973-01-01

    A cooperative ERTS-1 investigation involving U. S. Geological Survey, National Park Service, and Environmental Research Institure of Michigan (ERIM) personnel has as its goal the preparation of terrain classification maps for the entire Yellowstone National Park. Excellent coverage of the park was obtained on 6 August 1972 (frame 1015-17404). Preliminary terrain classification maps have been prepared at ERIM by applying multispectral pattern recognition techniques to ERTS-MSS digital taped data. The color coded terrain maps are presented and discussed. The discussion includes qualitative and quantitative accuracy estimates and discussion of processing techniques.

  8. Kernel earth mover's distance for EEG classification.

    PubMed

    Daliri, Mohammad Reza

    2013-07-01

    Here, we propose a new kernel approach based on the earth mover's distance (EMD) for electroencephalography (EEG) signal classification. The EEG time series are first transformed into histograms in this approach. The distance between these histograms is then computed using the EMD in a pair-wise manner. We bring the distances into a kernel form called kernel EMD. The support vector classifier can then be used for the classification of EEG signals. The experimental results on the real EEG data show that the new kernel method is very effective, and can classify the data with higher accuracy than traditional methods.

  9. Fuzzy set classifier for waste classification tracking

    SciTech Connect

    Gavel, D.T.

    1992-11-04

    We have developed an expert system based on fuzzy logic theory to fuse the data from multiple sensors and make classification decisions for objects in a waste reprocessing stream. Fuzzy set theory has been applied in decision and control applications with some success, particularly by the Japanese. We have found that the fuzzy logic system is rather easy to design and train, a feature that can cut development costs considerably. With proper training, the classification accuracy is quite high. We performed several tests sorting radioactive test samples using a gamma spectrometer to compare fuzzy logic to more conventional sorting schemes.

  10. Evaluation of gene expression classification studies: factors associated with classification performance.

    PubMed

    Novianti, Putri W; Roes, Kit C B; Eijkemans, Marinus J C

    2014-01-01

    Classification methods used in microarray studies for gene expression are diverse in the way they deal with the underlying complexity of the data, as well as in the technique used to build the classification model. The MAQC II study on cancer classification problems has found that performance was affected by factors such as the classification algorithm, cross validation method, number of genes, and gene selection method. In this paper, we study the hypothesis that the disease under study significantly determines which method is optimal, and that additionally sample size, class imbalance, type of medical question (diagnostic, prognostic or treatment response), and microarray platform are potentially influential. A systematic literature review was used to extract the information from 48 published articles on non-cancer microarray classification studies. The impact of the various factors on the reported classification accuracy was analyzed through random-intercept logistic regression. The type of medical question and method of cross validation dominated the explained variation in accuracy among studies, followed by disease category and microarray platform. In total, 42% of the between study variation was explained by all the study specific and problem specific factors that we studied together.

  11. Reticence, Accuracy and Efficacy

    NASA Astrophysics Data System (ADS)

    Oreskes, N.; Lewandowsky, S.

    2015-12-01

    James Hansen has cautioned the scientific community against "reticence," by which he means a reluctance to speak in public about the threat of climate change. This may contribute to social inaction, with the result that society fails to respond appropriately to threats that are well understood scientifically. Against this, others have warned against the dangers of "crying wolf," suggesting that reticence protects scientific credibility. We argue that both these positions are missing an important point: that reticence is not only a matter of style but also of substance. In previous work, Bysse et al. (2013) showed that scientific projections of key indicators of climate change have been skewed towards the low end of actual events, suggesting a bias in scientific work. More recently, we have shown that scientific efforts to be responsive to contrarian challenges have led scientists to adopt the terminology of a "pause" or "hiatus" in climate warming, despite the lack of evidence to support such a conclusion (Lewandowsky et al., 2015a. 2015b). In the former case, scientific conservatism has led to under-estimation of climate related changes. In the latter case, the use of misleading terminology has perpetuated scientific misunderstanding and hindered effective communication. Scientific communication should embody two equally important goals: 1) accuracy in communicating scientific information and 2) efficacy in expressing what that information means. Scientists should strive to be neither conservative nor adventurous but to be accurate, and to communicate that accurate information effectively.

  12. Automatic classification of blank substrate defects

    NASA Astrophysics Data System (ADS)

    Boettiger, Tom; Buck, Peter; Paninjath, Sankaranarayanan; Pereira, Mark; Ronald, Rob; Rost, Dan; Samir, Bhamidipati

    2014-10-01

    Mask preparation stages are crucial in mask manufacturing, since this mask is to later act as a template for considerable number of dies on wafer. Defects on the initial blank substrate, and subsequent cleaned and coated substrates, can have a profound impact on the usability of the finished mask. This emphasizes the need for early and accurate identification of blank substrate defects and the risk they pose to the patterned reticle. While Automatic Defect Classification (ADC) is a well-developed technology for inspection and analysis of defects on patterned wafers and masks in the semiconductors industry, ADC for mask blanks is still in the early stages of adoption and development. Calibre ADC is a powerful analysis tool for fast, accurate, consistent and automatic classification of defects on mask blanks. Accurate, automated classification of mask blanks leads to better usability of blanks by enabling defect avoidance technologies during mask writing. Detailed information on blank defects can help to select appropriate job-decks to be written on the mask by defect avoidance tools [1][4][5]. Smart algorithms separate critical defects from the potentially large number of non-critical defects or false defects detected at various stages during mask blank preparation. Mechanisms used by Calibre ADC to identify and characterize defects include defect location and size, signal polarity (dark, bright) in both transmitted and reflected review images, distinguishing defect signals from background noise in defect images. The Calibre ADC engine then uses a decision tree to translate this information into a defect classification code. Using this automated process improves classification accuracy, repeatability and speed, while avoiding the subjectivity of human judgment compared to the alternative of manual defect classification by trained personnel [2]. This paper focuses on the results from the evaluation of Automatic Defect Classification (ADC) product at MP Mask

  13. Piagetian Tasks, Traditional Intelligence and Achievement Tests.

    ERIC Educational Resources Information Center

    Kingma, J.; Koops, W.

    1983-01-01

    Reports study which compared the value of Piagetian tasks--seriation, conservation and multiple classification--to that of traditional intelligence tests--Cattell and PMA 5 to 7 subtests--as predictors of number language, simple computation, and verbal arithmetic achievement in 312 children from kindergarten to grade 4. Fifty references are…

  14. Object-based approaches to image classification for hyperspatial and hyperspectral data

    NASA Astrophysics Data System (ADS)

    Sridharan, Harini

    classifications as well as detailed urban forest tree species classifications were performed to test the performance of the classifier. The results for the two study areas show that the proposed classifier consistently achieves high accuracies, irrespective of the sensor, and also demonstrates superior performance in comparison to other popular object and pixel-based classifiers.

  15. Research Study of the 1998 Civics NAEP Achievement Levels.

    ERIC Educational Resources Information Center

    Loomis, Susan Cooper

    In 1999 a validation study of the 1998 Civics Assessment of the National Assessment of Educational Progress (NAEP) was conducted, combining a similarity classification study and a booklet classification study. The rationale was that if the teachers who had participated in the 5-day achievement levels-setting process could not use the descriptions…

  16. A Novel Modulation Classification Approach Using Gabor Filter Network

    PubMed Central

    Ghauri, Sajjad Ahmed; Qureshi, Ijaz Mansoor; Cheema, Tanveer Ahmed; Malik, Aqdas Naveed

    2014-01-01

    A Gabor filter network based approach is used for feature extraction and classification of digital modulated signals by adaptively tuning the parameters of Gabor filter network. Modulation classification of digitally modulated signals is done under the influence of additive white Gaussian noise (AWGN). The modulations considered for the classification purpose are PSK 2 to 64, FSK 2 to 64, and QAM 4 to 64. The Gabor filter network uses the network structure of two layers; the first layer which is input layer constitutes the adaptive feature extraction part and the second layer constitutes the signal classification part. The Gabor atom parameters are tuned using Delta rule and updating of weights of Gabor filter using least mean square (LMS) algorithm. The simulation results show that proposed novel modulation classification algorithm has high classification accuracy at low signal to noise ratio (SNR) on AWGN channel. PMID:25126603

  17. Text Classification by Combining Different Distance Functions with Weights

    NASA Astrophysics Data System (ADS)

    Yamada, Takahiro; Ishii, Naohiro; Nakashima, Toyoshiro

    The text classification is an important subject in the data mining. For the text classification, several methods have been developed up to now, as the nearest neighbor analysis, the latent semantic analysis, etc. The k-nearest neighbor (kNN) classification is a well-known simple and effective method for the classification of data in many domains. In the use of the kNN, the distance function is important to measure the distance and the similarity between data. To improve the performance of the classifier by the kNN, a new approach to combine multiple distance functions is proposed here. The weighting factors of elements in the distance function, are computed by GA for the effectiveness of the measurement. Further, an ensemble processing was developed for the improvement of the classification accuracy. Finally, it is shown by experiments that the methods, developed here, are effective in the text classification.

  18. Automated Classification and Correlation of Drill Cores using High-Resolution Hyperspectral Images and Supervised Pattern Classification Algorithms. Applications to Paleoseismology

    NASA Astrophysics Data System (ADS)

    Ragona, D. E.; Minster, B.; Rockwell, T.; Jasso, H.

    2006-12-01

    -layer perceptron with variable architecture and a Naive Bayesian classifier were trained to construct the classification models. Then, we computed the classification accuracy of our models using the validation sets. For the original reflectance set, our best model achieved a 98.4 % classification accuracy. The 2-norm normalized reflectance training set produced 97 % classification accuracy and the derivatives training sets generated models with 97 % (every point derivative) and 97.6 % (every five points derivative) classification accuracy. The outputs of the best model were used to classify the core images. We generated classification images of all the samples and compared them against the real samples for an ultimate qualitative verification of the classification. The results of this work show that reflectance spectra combined with neural networks or Naive Bayesian classifieres can be used to properly discern and classify sediments of very similar composition and grain size. Quantitative identification of geological materials can be used as a fast and objective method to describe samples, drill cores, trench exposures and outcrops.

  19. AVHRR composite period selection for land cover classification

    USGS Publications Warehouse

    Maxwell, S.K.; Hoffer, R.M.; Chapman, P.L.

    2002-01-01

    Multitemporal satellite image datasets provide valuable information on the phenological characteristics of vegetation, thereby significantly increasing the accuracy of cover type classifications compared to single date classifications. However, the processing of these datasets can become very complex when dealing with multitemporal data combined with multispectral data. Advanced Very High Resolution Radiometer (AVHRR) biweekly composite data are commonly used to classify land cover over large regions. Selecting a subset of these biweekly composite periods may be required to reduce the complexity and cost of land cover mapping. The objective of our research was to evaluate the effect of reducing the number of composite periods and altering the spacing of those composite periods on classification accuracy. Because inter-annual variability can have a major impact on classification results, 5 years of AVHRR data were evaluated. AVHRR biweekly composite images for spectral channels 1-4 (visible, near-infrared and two thermal bands) covering the entire growing season were used to classify 14 cover types over the entire state of Colorado for each of five different years. A supervised classification method was applied to maintain consistent procedures for each case tested. Results indicate that the number of composite periods can be halved-reduced from 14 composite dates to seven composite dates-without significantly reducing overall classification accuracy (80.4% Kappa accuracy for the 14-composite data-set as compared to 80.0% for a seven-composite dataset). At least seven composite periods were required to ensure the classification accuracy was not affected by inter-annual variability due to climate fluctuations. Concentrating more composites near the beginning and end of the growing season, as compared to using evenly spaced time periods, consistently produced slightly higher classification values over the 5 years tested (average Kappa) of 80.3% for the heavy early

  20. Field Accuracy Test of Rpas Photogrammetry

    NASA Astrophysics Data System (ADS)

    Barry, P.; Coakley, R.

    2013-08-01

    Baseline Surveys Ltd is a company which specialises in the supply of accurate geospatial data, such as cadastral, topographic and engineering survey data to commercial and government bodies. Baseline Surveys Ltd invested in aerial drone photogrammetric technology and had a requirement to establish the spatial accuracy of the geographic data derived from our unmanned aerial vehicle (UAV) photogrammetry before marketing our new aerial mapping service. Having supplied the construction industry with survey data for over 20 years, we felt that is was crucial for our clients to clearly understand the accuracy of our photogrammetry so they can safely make informed spatial decisions, within the known accuracy limitations of our data. This information would also inform us on how and where UAV photogrammetry can be utilised. What we wanted to find out was the actual accuracy that can be reliably achieved using a UAV to collect data under field conditions throughout a 2 Ha site. We flew a UAV over the test area in a "lawnmower track" pattern with an 80% front and 80% side overlap; we placed 45 ground markers as check points and surveyed them in using network Real Time Kinematic Global Positioning System (RTK GPS). We specifically designed the ground markers to meet our accuracy needs. We established 10 separate ground markers as control points and inputted these into our photo modelling software, Agisoft PhotoScan. The remaining GPS coordinated check point data were added later in ArcMap to the completed orthomosaic and digital elevation model so we could accurately compare the UAV photogrammetry XYZ data with the RTK GPS XYZ data at highly reliable common points. The accuracy we achieved throughout the 45 check points was 95% reliably within 41 mm horizontally and 68 mm vertically and with an 11.7 mm ground sample distance taken from a flight altitude above ground level of 90 m.The area covered by one image was 70.2 m × 46.4 m, which equals 0.325 Ha. This finding has shown

  1. Vehicle classification in WAMI imagery using deep network

    NASA Astrophysics Data System (ADS)

    Yi, Meng; Yang, Fan; Blasch, Erik; Sheaff, Carolyn; Liu, Kui; Chen, Genshe; Ling, Haibin

    2016-05-01

    Humans have always had a keen interest in understanding activities and the surrounding environment for mobility, communication, and survival. Thanks to recent progress in photography and breakthroughs in aviation, we are now able to capture tens of megapixels of ground imagery, namely Wide Area Motion Imagery (WAMI), at multiple frames per second from unmanned aerial vehicles (UAVs). WAMI serves as a great source for many applications, including security, urban planning and route planning. These applications require fast and accurate image understanding which is time consuming for humans, due to the large data volume and city-scale area coverage. Therefore, automatic processing and understanding of WAMI imagery has been gaining attention in both industry and the research community. This paper focuses on an essential step in WAMI imagery analysis, namely vehicle classification. That is, deciding whether a certain image patch contains a vehicle or not. We collect a set of positive and negative sample image patches, for training and testing the detector. Positive samples are 64 × 64 image patches centered on annotated vehicles. We generate two sets of negative images. The first set is generated from positive images with some location shift. The second set of negative patches is generated from randomly sampled patches. We also discard those patches if a vehicle accidentally locates at the center. Both positive and negative samples are randomly divided into 9000 training images and 3000 testing images. We propose to train a deep convolution network for classifying these patches. The classifier is based on a pre-trained AlexNet Model in the Caffe library, with an adapted loss function for vehicle classification. The performance of our classifier is compared to several traditional image classifier methods using Support Vector Machine (SVM) and Histogram of Oriented Gradient (HOG) features. While the SVM+HOG method achieves an accuracy of 91.2%, the accuracy of our deep

  2. Classification, disease, and diagnosis.

    PubMed

    Jutel, Annemarie

    2011-01-01

    Classification shapes medicine and guides its practice. Understanding classification must be part of the quest to better understand the social context and implications of diagnosis. Classifications are part of the human work that provides a foundation for the recognition and study of illness: deciding how the vast expanse of nature can be partitioned into meaningful chunks, stabilizing and structuring what is otherwise disordered. This article explores the aims of classification, their embodiment in medical diagnosis, and the historical traditions of medical classification. It provides a brief overview of the aims and principles of classification and their relevance to contemporary medicine. It also demonstrates how classifications operate as social framing devices that enable and disable communication, assert and refute authority, and are important items for sociological study. PMID:21532133

  3. Classification, disease, and diagnosis.

    PubMed

    Jutel, Annemarie

    2011-01-01

    Classification shapes medicine and guides its practice. Understanding classification must be part of the quest to better understand the social context and implications of diagnosis. Classifications are part of the human work that provides a foundation for the recognition and study of illness: deciding how the vast expanse of nature can be partitioned into meaningful chunks, stabilizing and structuring what is otherwise disordered. This article explores the aims of classification, their embodiment in medical diagnosis, and the historical traditions of medical classification. It provides a brief overview of the aims and principles of classification and their relevance to contemporary medicine. It also demonstrates how classifications operate as social framing devices that enable and disable communication, assert and refute authority, and are important items for sociological study.

  4. ANALYSIS OF A CLASSIFICATION ERROR MATRIX USING CATEGORICAL DATA TECHNIQUES.

    USGS Publications Warehouse

    Rosenfield, George H.; Fitzpatrick-Lins, Katherine

    1984-01-01

    Summary form only given. A classification error matrix typically contains tabulation results of an accuracy evaluation of a thematic classification, such as that of a land use and land cover map. The diagonal elements of the matrix represent the counts corrected, and the usual designation of classification accuracy has been the total percent correct. The nondiagonal elements of the matrix have usually been neglected. The classification error matrix is known in statistical terms as a contingency table of categorical data. As an example, an application of these methodologies to a problem of remotely sensed data concerning two photointerpreters and four categories of classification indicated that there is no significant difference in the interpretation between the two photointerpreters, and that there are significant differences among the interpreted category classifications. However, two categories, oak and cottonwood, are not separable in classification in this experiment at the 0. 51 percent probability. A coefficient of agreement is determined for the interpreted map as a whole, and individually for each of the interpreted categories. A conditional coefficient of agreement for the individual categories is compared to other methods for expressing category accuracy which have already been presented in the remote sensing literature.

  5. Extreme learning machine and adaptive sparse representation for image classification.

    PubMed

    Cao, Jiuwen; Zhang, Kai; Luo, Minxia; Yin, Chun; Lai, Xiaoping

    2016-09-01

    Recent research has shown the speed advantage of extreme learning machine (ELM) and the accuracy advantage of sparse representation classification (SRC) in the area of image classification. Those two methods, however, have their respective drawbacks, e.g., in general, ELM is known to be less robust to noise while SRC is known to be time-consuming. Consequently, ELM and SRC complement each other in computational complexity and classification accuracy. In order to unify such mutual complementarity and thus further enhance the classification performance, we propose an efficient hybrid classifier to exploit the advantages of ELM and SRC in this paper. More precisely, the proposed classifier consists of two stages: first, an ELM network is trained by supervised learning. Second, a discriminative criterion about the reliability of the obtained ELM output is adopted to decide whether the query image can be correctly classified or not. If the output is reliable, the classification will be performed by ELM; otherwise the query image will be fed to SRC. Meanwhile, in the stage of SRC, a sub-dictionary that is adaptive to the query image instead of the entire dictionary is extracted via the ELM output. The computational burden of SRC thus can be reduced. Extensive experiments on handwritten digit classification, landmark recognition and face recognition demonstrate that the proposed hybrid classifier outperforms ELM and SRC in classification accuracy with outstanding computational efficiency.

  6. Extreme learning machine and adaptive sparse representation for image classification.

    PubMed

    Cao, Jiuwen; Zhang, Kai; Luo, Minxia; Yin, Chun; Lai, Xiaoping

    2016-09-01

    Recent research has shown the speed advantage of extreme learning machine (ELM) and the accuracy advantage of sparse representation classification (SRC) in the area of image classification. Those two methods, however, have their respective drawbacks, e.g., in general, ELM is known to be less robust to noise while SRC is known to be time-consuming. Consequently, ELM and SRC complement each other in computational complexity and classification accuracy. In order to unify such mutual complementarity and thus further enhance the classification performance, we propose an efficient hybrid classifier to exploit the advantages of ELM and SRC in this paper. More precisely, the proposed classifier consists of two stages: first, an ELM network is trained by supervised learning. Second, a discriminative criterion about the reliability of the obtained ELM output is adopted to decide whether the query image can be correctly classified or not. If the output is reliable, the classification will be performed by ELM; otherwise the query image will be fed to SRC. Meanwhile, in the stage of SRC, a sub-dictionary that is adaptive to the query image instead of the entire dictionary is extracted via the ELM output. The computational burden of SRC thus can be reduced. Extensive experiments on handwritten digit classification, landmark recognition and face recognition demonstrate that the proposed hybrid classifier outperforms ELM and SRC in classification accuracy with outstanding computational efficiency. PMID:27389571

  7. Individual subject classification for Alzheimer's disease based on incremental learning using a spatial frequency representation of cortical thickness data.

    PubMed

    Cho, Youngsang; Seong, Joon-Kyung; Jeong, Yong; Shin, Sung Yong

    2012-02-01

    Patterns of brain atrophy measured by magnetic resonance structural imaging have been utilized as significant biomarkers for diagnosis of Alzheimer's disease (AD). However, brain atrophy is variable across patients and is non-specific for AD in general. Thus, automatic methods for AD classification require a large number of structural data due to complex and variable patterns of brain atrophy. In this paper, we propose an incremental method for AD classification using cortical thickness data. We represent the cortical thickness data of a subject in terms of their spatial frequency components, employing the manifold harmonic transform. The basis functions for this transform are obtained from the eigenfunctions of the Laplace-Beltrami operator, which are dependent only on the geometry of a cortical surface but not on the cortical thickness defined on it. This facilitates individual subject classification based on incremental learning. In general, methods based on region-wise features poorly reflect the detailed spatial variation of cortical thickness, and those based on vertex-wise features are sensitive to noise. Adopting a vertex-wise cortical thickness representation, our method can still achieve robustness to noise by filtering out high frequency components of the cortical thickness data while reflecting their spatial variation. This compromise leads to high accuracy in AD classification. We utilized MR volumes provided by Alzheimer's Disease Neuroimaging Initiative (ADNI) to validate the performance of the method. Our method discriminated AD patients from Healthy Control (HC) subjects with 82% sensitivity and 93% specificity. It also discriminated Mild Cognitive Impairment (MCI) patients, who converted to AD within 18 months, from non-converted MCI subjects with 63% sensitivity and 76% specificity. Moreover, it showed that the entorhinal cortex was the most discriminative region for classification, which is consistent with previous pathological findings. In

  8. Computer classification of remotely sensed multispectral image data by extraction and classification of homogeneous objects

    NASA Technical Reports Server (NTRS)

    Kettig, R. L.

    1975-01-01

    A method of classification of digitized multispectral images is developed and experimentally evaluated on actual earth resources data collected by aircraft and satellite. The method is designed to exploit the characteristic dependence between adjacent states of nature that is neglected by the more conventional simple-symmetric decision rule. Thus contextual information is incorporated into the classification scheme. The principle reason for doing this is to improve the accuracy of the classification. For general types of dependence this would generally require more computation per resolution element than the simple-symmetric classifier. But when the dependence occurs in the form of redundance, the elements can be classified collectively, in groups, therby reducing the number of classifications required.

  9. Weighted Dynamic Time Warping for Time Series Classification

    SciTech Connect

    Jeong, Young-Seon; Jeong, Myong K; Omitaomu, Olufemi A

    2011-01-01

    Dynamic time warping (DTW), which finds the minimum path by providing non-linear alignments between two time series, has been widely used as a distance measure for time series classification and clustering. However, DTW does not account for the relative importance regarding the phase difference between a reference point and a testing point. This may lead to misclassification especially in applications where the shape similarity between two sequences is a major consideration for an accurate recognition. Therefore, we propose a novel distance measure, called a weighted DTW (WDTW), which is a penalty-based DTW. Our approach penalizes points with higher phase difference between a reference point and a testing point in order to prevent minimum distance distortion caused by outliers. The rationale underlying the proposed distance measure is demonstrated with some illustrative examples. A new weight function, called the modified logistic weight function (MLWF), is also proposed to systematically assign weights as a function of the phase difference between a reference point and a testing point. By applying different weights to adjacent points, the proposed algorithm can enhance the detection of similarity between two time series. We show that some popular distance measures such as DTW and Euclidean distance are special cases of our proposed WDTW measure. We extend the proposed idea to other variants of DTW such as derivative dynamic time warping (DDTW) and propose the weighted version of DDTW. We have compared the performances of our proposed procedures with other popular approaches using public data sets available through the UCR Time Series Data Mining Archive for both time series classification and clustering problems. The experimental results indicate that the proposed approaches can achieve improved accuracy for time series classification and clustering problems.

  10. Vertebral classification using localized pathology-related shape model

    NASA Astrophysics Data System (ADS)

    Zewail, R.; Elsafi, A.; Durdle, N.

    2008-03-01

    Radiographs of the spine are frequently examined for assessment of vertebral abnormalities. Features like osteophytes (bony growth of vertebra's corners), and disc space narrowing are often used as visual evidence of osteoarthris or degenerative joint disease. These symptoms result in remarkable changes in the shapes of the vertebral body. Statistical analysis of anatomical structure has recently gained increased popularity within the medical imaging community, since they have the potential to enhance the automated diagnosis process. In this paper, we present a novel method for computer-assisted vertebral classification using a localized, pathology-related shape model. The new classification scheme is able to assess the condition of multiple vertebrae simultaneously, hence is possible to directly classify the whole spine anatomy according to the condition of interest (anterior osteophites). At the core of this method is a new localized shape model that uses concepts of sparsity, dimension reduction, and statistical independence to extract sets of localized modes of deformations specific to each of the vertebrae under investigation. By projection of the shapes onto any specific set of deformation modes (or basis), we obtain low-dimensional features that are most directly related to the pathology of the vertebra of interest. These features are then used as input to a support vector machine classifier to classify the vertebra under investigation as normal or upnormal. Experiments are conducted using contours from digital x-ray images of five vertebrae of lumbar spine. The accuracy of the classification scheme is assessed using the ROC curves. An average specifity of 96.8 % is achieved with a sensitivity of 80 %.

  11. Sparse Multivariate Autoregressive Modeling for Mild Cognitive Impairment Classification

    PubMed Central

    Li, Yang; Wee, Chong-Yaw; Jie, Biao; Peng, Ziwen

    2014-01-01

    Brain connectivity network derived from functional magnetic resonance imaging (fMRI) is becoming increasingly prevalent in the researches related to cognitive and perceptual processes. The capability to detect causal or effective connectivity is highly desirable for understanding the cooperative nature of brain network, particularly when the ultimate goal is to obtain good performance of control-patient classification with biological meaningful interpretations. Understanding directed functional interactions between brain regions via brain connectivity network is a challenging task. Since many genetic and biomedical networks are intrinsically sparse, incorporating sparsity property into connectivity modeling can make the derived models more biologically plausible. Accordingly, we propose an effective connectivity modeling of resting-state fMRI data based on the multivariate autoregressive (MAR) modeling technique, which is widely used to characterize temporal information of dynamic systems. This MAR modeling technique allows for the identification of effective connectivity using the Granger causality concept and reducing the spurious causality connectivity in assessment of directed functional interaction from fMRI data. A forward orthogonal least squares (OLS) regression algorithm is further used to construct a sparse MAR model. By applying the proposed modeling to mild cognitive impairment (MCI) classification, we identify several most discriminative regions, including middle cingulate gyrus, posterior cingulate gyrus, lingual gyrus and caudate regions, in line with results reported in previous findings. A relatively high classification accuracy of 91.89 % is also achieved, with an increment of 5.4 % compared to the fully-connected, non-directional Pearson-correlation-based functional connectivity approach. PMID:24595922

  12. Applications of pattern classification to time-domain signals

    NASA Astrophysics Data System (ADS)

    Bertoncini, Crystal Ann

    Many different kinds of physics are used in sensors that produce time-domain signals, such as ultrasonics, acoustics, seismology, and electromagnetics. The waveforms generated by these sensors are used to measure events or detect flaws in applications ranging from industrial to medical and defense-related domains. Interpreting the signals is challenging because of the complicated physics of the interaction of the fields with the materials and structures under study. Often the method of interpreting the signal varies by the application, but automatic detection of events in signals is always useful in order to attain results quickly with less human error. One method of automatic interpretation of data is pattern classification, which is a statistical method that assigns predicted labels to raw data associated with known categories. In this work, we use pattern classification techniques to aid automatic detection of events in signals using features extracted by a particular application of the wavelet transform, the Dynamic Wavelet Fingerprint (DWFP), as well as features selected through physical interpretation of the individual applications. The wavelet feature extraction method is general for any time-domain signal, and the classification results can be improved by features drawn for the particular domain. The success of this technique is demonstrated through four applications: the development of an ultrasonographic periodontal probe, the identification of flaw type in Lamb wave tomographic scans of an aluminum pipe, prediction of roof falls in a limestone mine, and automatic identification of individual Radio Frequency Identification (RFID) tags regardless of its programmed code. The method has been shown to achieve high accuracy, sometimes as high as 98%.

  13. Genetic Bee Colony (GBC) algorithm: A new gene selection method for microarray cancer classification.

    PubMed

    Alshamlan, Hala M; Badr, Ghada H; Alohali, Yousef A

    2015-06-01

    Naturally inspired evolutionary algorithms prove effectiveness when used for solving feature selection and classification problems. Artificial Bee Colony (ABC) is a relatively new swarm intelligence method. In this paper, we propose a new hybrid gene selection method, namely Genetic Bee Colony (GBC) algorithm. The proposed algorithm combines the used of a Genetic Algorithm (GA) along with Artificial Bee Colony (ABC) algorithm. The goal is to integrate the advantages of both algorithms. The proposed algorithm is applied to a microarray gene expression profile in order to select the most predictive and informative genes for cancer classification. In order to test the accuracy performance of the proposed algorithm, extensive experiments were conducted. Three binary microarray datasets are use, which include: colon, leukemia, and lung. In addition, another three multi-class microarray datasets are used, which are: SRBCT, lymphoma, and leukemia. Results of the GBC algorithm are compared with our recently proposed technique: mRMR when combined with the Artificial Bee Colony algorithm (mRMR-ABC). We also compared the combination of mRMR with GA (mRMR-GA) and Particle Swarm Optimization (mRMR-PSO) algorithms. In addition, we compared the GBC algorithm with other related algorithms that have been recently published in the literature, using all benchmark datasets. The GBC algorithm shows superior performance as it achieved the highest classification accuracy along with the lowest average number of selected genes. This proves that the GBC algorithm is a promising approach for solving the gene selection problem in both binary and multi-class cancer classification.

  14. Comparison of Standard and Novel Signal Analysis Approaches to Obstructive Sleep Apnea Classification

    PubMed Central

    Roebuck, Aoife; Clifford, Gari D.

    2015-01-01

    Obstructive sleep apnea (OSA) is a disorder characterized by repeated pauses in breathing during sleep, which leads to deoxygenation and voiced chokes at the end of each episode. OSA is associated by daytime sleepiness and an increased risk of serious conditions such as cardiovascular disease, diabetes, and stroke. Between 2 and 7% of the adult population globally has OSA, but it is estimated that up to 90% of those are undiagnosed and untreated. Diagnosis of OSA requires expensive and cumbersome screening. Audio offers a potential non-contact alternative, particularly with the ubiquity of excellent signal processing on every phone. Previous studies have focused on the classification of snoring and apneic chokes. However, such approaches require accurate identification of events. This leads to limited accuracy and small study populations. In this work, we propose an alternative approach which uses multiscale entropy (MSE) coefficients presented to a classifier to identify disorder in vocal patterns indicative of sleep apnea. A database of 858 patients was used, the largest reported in this domain. Apneic choke, snore, and noise events encoded with speech analysis features were input into a linear classifier. Coefficients of MSE derived from the first 4 h of each recording were used to train and test a random forest to classify patients as apneic or not. Standard speech analysis approaches for event classification achieved an out-of-sample accuracy (Ac) of 76.9% with a sensitivity (Se) of 29.2% and a specificity (Sp) of 88.7% but high variance. For OSA severity classification, MSE provided an out-of-sample Ac of 79.9%, Se of 66.0%, and Sp = 88.8%. Including demographic information improved the MSE-based classification performance to Ac = 80.5%, Se = 69.2%, and Sp = 87.9%. These results indicate that audio recordings could be used in screening for OSA, but are generally under-sensitive. PMID:26380256

  15. Feature extraction and classification algorithms for high dimensional data

    NASA Technical Reports Server (NTRS)

    Lee, Chulhee; Landgrebe, David

    1993-01-01

    Feature extraction and classification algorithms for high dimensional data are investigated. Developments with regard to sensors for Earth observation are moving in the direction of providing much higher dimensional multispectral imagery than is now possible. In analyzing such high dimensional data, processing time becomes an important factor. With large increases in dimensionality and the number of classes, processing time will increase significantly. To address this problem, a multistage classification scheme is proposed which reduces the processing time substantially by eliminating unlikely classes from further consideration at each stage. Several truncation criteria are developed and the relationship between thresholds and the error caused by the truncation is investigated. Next an approach to feature extraction for classification is proposed based directly on the decision boundaries. It is shown that all the features needed for classification can be extracted from decision boundaries. A characteristic of the proposed method arises by noting that only a portion of the decision boundary is effective in discriminating between classes, and the concept of the effective decision boundary is introduced. The proposed feature extraction algorithm has several desirable properties: it predicts the minimum number of features necessary to achieve the same classification accuracy as in the original space for a given pattern recognition problem; and it finds the necessary feature vectors. The proposed algorithm does not deteriorate under the circumstances of equal means or equal covariances as some previous algorithms do. In addition, the decision boundary feature extraction algorithm can be used both for parametric and non-parametric classifiers. Finally, some problems encountered in analyzing high dimensional data are studied and possible solutions are proposed. First, the increased importance of the second order statistics in analyzing high dimensional data is recognized

  16. A Classification of Remote Sensing Image Based on Improved Compound Kernels of Svm

    NASA Astrophysics Data System (ADS)

    Zhao, Jianing; Gao, Wanlin; Liu, Zili; Mou, Guifen; Lu, Lin; Yu, Lina

    The accuracy of RS classification based on SVM which is developed from statistical learning theory is high under small number of train samples, which results in satisfaction of classification on RS using SVM methods. The traditional RS classification method combines visual interpretation with computer classification. The accuracy of the RS classification, however, is improved a lot based on SVM method, because it saves much labor and time which is used to interpret images and collect training samples. Kernel functions play an important part in the SVM algorithm. It uses improved compound kernel function and therefore has a higher accuracy of classification on RS images. Moreover, compound kernel improves the generalization and learning ability of the kernel.

  17. Graded Achievement, Tested Achievement, and Validity

    ERIC Educational Resources Information Center

    Brookhart, Susan M.

    2015-01-01

    Twenty-eight studies of grades, over a century, were reviewed using the argument-based approach to validity suggested by Kane as a theoretical framework. The review draws conclusions about the meaning of graded achievement, its relation to tested achievement, and changes in the construct of graded achievement over time. "Graded…

  18. Regional-scale grassland classification using moderate-resolution imaging spectrometer datasets based on multistep unsupervised classification and indices suitability analysis

    NASA Astrophysics Data System (ADS)

    Yang, Xuemei; Yang, Taibao; Ji, Qin; He, Yi; Ghebrezgabher, Mihretab G.

    2014-01-01

    This study used the normalized difference vegetation index (NDVI), in conjunction with other ancillary indices, a digital elevation model (DEM), and the multistep unsupervised classification method to classify grassland in Gansu Province and the Qilian Mountains in China. The results showed that the overall accuracy of vegetation type reached 88.79% and that of grassland coverage level reached 87.23%. The ancillary indices suitability analysis revealed that meadow was distributed mainly in zones where the normalized difference moisture index (NDMI) varied between -0.64 and -0.4, whereas for steppe, it varied between -0.55 and -0.32. Grassland with a different coverage level was mainly distributed in zones where the normalized difference soil index (NDSI) varied between -0.20 and 0.25. To demonstrate the usability of these two indices, the maximum values of NDVI, NDMI, and NDSI and the DEM were used in the decision tree classification method for grassland. The results achieved relatively high kappa coefficients of 77.09% for vegetation type and 65.29% for grassland coverage level. Based on these results, it can be concluded that it is rational to apply the multistep unsupervised classification method and the selected indices for regional-scale grassland identification when a priori information is scarce, expensive, or unsuitable.

  19. Robust classification of neonatal apnoea-related desaturations.

    PubMed

    Monasterio, Violeta; Burgess, Fred; Clifford, Gari D

    2012-09-01

    Respiratory signals monitored in the neonatal intensive care units are usually ignored due to the high prevalence of noise and false alarms (FA). Apneic events are generally therefore indicated by a pulse oximeter alarm reacting to the subsequent desaturation. However, the high FA rate in the photoplethysmogram may desensitize staff, reducing the reaction speed. The main reason for the high FA rates of critical care monitors is the unimodal analysis behaviour. In this work, we propose a multimodal analysis framework to reduce the FA rate in neonatal apnoea monitoring. Information about oxygen saturation, heart rate, respiratory rate and signal quality was extracted from electrocardiogram, impedance pneumogram and photoplethysmographic signals for a total of 20 features in the 5 min interval before a desaturation event. 1616 desaturation events from 27 neonatal admissions were annotated by two independent reviewers as true (physiologically relevant) or false (noise-related). Patients were divided into two independent groups for training and validation, and a support vector machine was trained to classify the events as true or false. The best classification performance was achieved on a combination of 13 features with sensitivity, specificity and accuracy of 100% in the training set, and a sensitivity of 86%, a specificity of 91% and an accuracy of 90% in the validation set.

  20. The National Vegetation Classification Standard applied to the remote sensing classification of two semiarid environments.

    PubMed

    Ramsey, Elijah W; Nelson, Gene A; Echols, Darrell; Sapkota, Sijan K

    2002-05-01

    The National Vegetation Classification Standard (NVCS) was implemented at two US National Park Service (NPS) sites in Texas, the Padre Island National Seashore (PINS) and the Lake Meredith National Recreation Area (LMNRA), to provide information for NPS oil and gas management plans. Because NVCS landcover classifications did not exist for these two areas prior to this study, we created landcover classes, through intensive ground and aerial reconnaissance, that characterized the general landscape features and at the same time complied with NVCS guidelines. The created landcover classes were useful for the resource management and were conducive to classification with optical remote sensing systems, such as the Landsat Thematic Mapper (TM). In the LMNRA, topographic elevation data were added to the TM data to reduce confusion between cliff, high plains, and forest classes. Classification accuracies (kappa statistics) of 89.9% (0.89) and 88.2% (0.87) in PINS and LMNRA, respectively, verified that the two NPS landholdings were adequately mapped with TM data. Improved sensor systems with higher spectral and spatial resolutions will ultimately refine the broad classes defined in this classification; however, the landcover classifications created in this study have already provided valuable information for the management of both NPS lands. Habitat information provided by the classifications has aided in the placement of inventory and monitoring plots, has assisted oil and gas operators by providing information on sensitive habitats, and has allowed park managers to better use resources when fighting wildland fires and in protecting visitors and the infrastructure of NPS lands.

  1. Security classification of information

    SciTech Connect

    Quist, A.S.

    1989-09-01

    Certain governmental information must be classified for national security reasons. However, the national security benefits from classifying information are usually accompanied by significant costs -- those due to a citizenry not fully informed on governmental activities, the extra costs of operating classified programs and procuring classified materials (e.g., weapons), the losses to our nation when advances made in classified programs cannot be utilized in unclassified programs. The goal of a classification system should be to clearly identify that information which must be protected for national security reasons and to ensure that information not needing such protection is not classified. This document was prepared to help attain that goal. This document is the first of a planned four-volume work that comprehensively discusses the security classification of information. Volume 1 broadly describes the need for classification, the basis for classification, and the history of classification in the United States from colonial times until World War 2. Classification of information since World War 2, under Executive Orders and the Atomic Energy Acts of 1946 and 1954, is discussed in more detail, with particular emphasis on the classification of atomic energy information. Adverse impacts of classification are also described. Subsequent volumes will discuss classification principles, classification management, and the control of certain unclassified scientific and technical information. 340 refs., 6 tabs.

  2. Security classification of information

    SciTech Connect

    Quist, A.S.

    1993-04-01

    This document is the second of a planned four-volume work that comprehensively discusses the security classification of information. The main focus of Volume 2 is on the principles for classification of information. Included herein are descriptions of the two major types of information that governments classify for national security reasons (subjective and objective information), guidance to use when determining whether information under consideration for classification is controlled by the government (a necessary requirement for classification to be effective), information disclosure risks and benefits (the benefits and costs of classification), standards to use when balancing information disclosure risks and benefits, guidance for assigning classification levels (Top Secret, Secret, or Confidential) to classified information, guidance for determining how long information should be classified (classification duration), classification of associations of information, classification of compilations of information, and principles for declassifying and downgrading information. Rules or principles of certain areas of our legal system (e.g., trade secret law) are sometimes mentioned to .provide added support to some of those classification principles.

  3. Recursive heuristic classification

    NASA Technical Reports Server (NTRS)

    Wilkins, David C.

    1994-01-01

    The author will describe a new problem-solving approach called recursive heuristic classification, whereby a subproblem of heuristic classification is itself formulated and solved by heuristic classification. This allows the construction of more knowledge-intensive classification programs in a way that yields a clean organization. Further, standard knowledge acquisition and learning techniques for heuristic classification can be used to create, refine, and maintain the knowledge base associated with the recursively called classification expert system. The method of recursive heuristic classification was used in the Minerva blackboard shell for heuristic classification. Minerva recursively calls itself every problem-solving cycle to solve the important blackboard scheduler task, which involves assigning a desirability rating to alternative problem-solving actions. Knowing these ratings is critical to the use of an expert system as a component of a critiquing or apprenticeship tutoring system. One innovation of this research is a method called dynamic heuristic classification, which allows selection among dynamically generated classification categories instead of requiring them to be prenumerated.

  4. Adaptive neuro-fuzzy inference system for classification of background EEG signals from ESES patients and controls.

    PubMed

    Yang, Zhixian; Wang, Yinghua; Ouyang, Gaoxiang

    2014-01-01

    Background electroencephalography (EEG), recorded with scalp electrodes, in children with electrical status epilepticus during slow-wave sleep (ESES) syndrome and control subjects has been analyzed. We considered 10 ESES patients, all right-handed and aged 3-9 years. The 10 control individuals had the same characteristics of the ESES ones but presented a normal EEG. Recordings were undertaken in the awake and relaxed states with their eyes open. The complexity of background EEG was evaluated using the permutation entropy (PE) and sample entropy (SampEn) in combination with the ANOVA test. It can be seen that the entropy measures of EEG are significantly different between the ESES patients and normal control subjects. Then, a classification framework based on entropy measures and adaptive neuro-fuzzy inference system (ANFIS) classifier is proposed to distinguish ESES and normal EEG signals. The results are promising and a classification accuracy of about 89% is achieved. PMID:24790547

  5. Adaptive Neuro-Fuzzy Inference System for Classification of Background EEG Signals from ESES Patients and Controls

    PubMed Central

    Yang, Zhixian; Wang, Yinghua; Ouyang, Gaoxiang

    2014-01-01

    Background electroencephalography (EEG), recorded with scalp electrodes, in children with electrical status epilepticus during slow-wave sleep (ESES) syndrome and control subjects has been analyzed. We considered 10 ESES patients, all right-handed and aged 3–9 years. The 10 control individuals had the same characteristics of the ESES ones but presented a normal EEG. Recordings were undertaken in the awake and relaxed states with their eyes open. The complexity of background EEG was evaluated using the permutation entropy (PE) and sample entropy (SampEn) in combination with the ANOVA test. It can be seen that the entropy measures of EEG are significantly different between the ESES patients and normal control subjects. Then, a classification framework based on entropy measures and adaptive neuro-fuzzy inference system (ANFIS) classifier is proposed to distinguish ESES and normal EEG signals. The results are promising and a classification accuracy of about 89% is achieved. PMID:24790547

  6. An Automatic Segmentation and Classification Framework Based on PCNN Model for Single Tooth in MicroCT Images.

    PubMed

    Wang, Liansheng; Li, Shusheng; Chen, Rongzhen; Liu, Sze-Yu; Chen, Jyh-Cheng

    2016-01-01

    Accurate segmentation and classification of different anatomical structures of teeth from medical images plays an essential role in many clinical applications. Usually, the anatomical structures of teeth are manually labelled by experienced clinical doctors, which is time consuming. However, automatic segmentation and classification is a challenging task because the anatomical structures and surroundings of the tooth in medical images are rather complex. Therefore, in this paper, we propose an effective framework which is designed to segment the tooth with a Selective Binary and Gaussian Filtering Regularized Level Set (GFRLS) method improved by fully utilizing three dimensional (3D) information, and classify the tooth by employing unsupervised learning Pulse Coupled Neural Networks (PCNN) model. In order to evaluate the proposed method, the experiments are conducted on the different datasets of mandibular molars and the experimental results show that our method can achieve better accuracy and robustness compared to other four state of the art clustering methods. PMID:27322421

  7. An Automatic Segmentation and Classification Framework Based on PCNN Model for Single Tooth in MicroCT Images

    PubMed Central

    Wang, Liansheng; Li, Shusheng; Chen, Rongzhen; Liu, Sze-Yu; Chen, Jyh-Cheng

    2016-01-01

    Accurate segmentation and classification of different anatomical structures of teeth from medical images plays an essential role in many clinical applications. Usually, the anatomical structures of teeth are manually labelled by experienced clinical doctors, which is time consuming. However, automatic segmentation and classification is a challenging task because the anatomical structures and surroundings of the tooth in medical images are rather complex. Therefore, in this paper, we propose an effective framework which is designed to segment the tooth with a Selective Binary and Gaussian Filtering Regularized Level Set (GFRLS) method improved by fully utilizing three dimensional (3D) information, and classify the tooth by employing unsupervised learning Pulse Coupled Neural Networks (PCNN) model. In order to evaluate the proposed method, the experiments are conducted on the different datasets of mandibular molars and the experimental results show that our method can achieve better accuracy and robustness compared to other four state of the art clustering methods. PMID:27322421

  8. Graduates employment classification using data mining approach

    NASA Astrophysics Data System (ADS)

    Aziz, Mohd Tajul Rizal Ab; Yusof, Yuhanis

    2016-08-01

    Data Mining is a platform to extract hidden knowledge in a collection of data. This study investigates the suitable classification model to classify graduates employment for one of the MARA Professional College (KPM) in Malaysia. The aim is to classify the graduates into either as employed, unemployed or further study. Five data mining algorithms offered in WEKA were used; Naïve Bayes, Logistic regression, Multilayer perceptron, k-nearest neighbor and Decision tree J48. Based on the obtained result, it is learned that the Logistic regression produces the highest classification accuracy which is at 92.5%. Such result was obtained while using 80% data for training and 20% for testing. The produced classification model will benefit the management of the college as it provides insight to the quality of graduates that they produce and how their curriculum can be improved to cater the needs from the industry.

  9. Unsupervised classification of remote multispectral sensing data

    NASA Technical Reports Server (NTRS)

    Su, M. Y.

    1972-01-01

    The new unsupervised classification technique for classifying multispectral remote sensing data which can be either from the multispectral scanner or digitized color-separation aerial photographs consists of two parts: (a) a sequential statistical clustering which is a one-pass sequential variance analysis and (b) a generalized K-means clustering. In this composite clustering technique, the output of (a) is a set of initial clusters which are input to (b) for further improvement by an iterative scheme. Applications of the technique using an IBM-7094 computer on multispectral data sets over Purdue's Flight Line C-1 and the Yellowstone National Park test site have been accomplished. Comparisons between the classification maps by the unsupervised technique and the supervised maximum liklihood technique indicate that the classification accuracies are in agreement.

  10. Corn and soybean Landsat MSS classification performance as a function of scene characteristics

    NASA Technical Reports Server (NTRS)

    Batista, G. T.; Hixson, M. M.; Bauer, M. E.

    1982-01-01

    In order to fully utilize remote sensing to inventory crop production, it is important to identify the factors that affect the accuracy of Landsat classifications. The objective of this study was to investigate the effect of scene characteristics involving crop, soil, and weather variables on the accuracy of Landsat classifications of corn and soybeans. Segments sampling the U.S. Corn Belt were classified using a Gaussian maximum likelihood classifier on multitemporally registered data from two key acquisition periods. Field size had a strong effect on classification accuracy with small fields tending to have low accuracies even when the effect of mixed pixels was eliminated. Other scene characteristics accounting for variability in classification accuracy included proportions of corn and soybeans, crop diversity index, proportion of all field crops, soil drainage, slope, soil order, long-term average soybean yield, maximum yield, relative position of the segment in the Corn Belt, weather, and crop development stage.

  11. Diagnostic Accuracy Comparison of Artificial Immune Algorithms for Primary Headaches.

    PubMed

    Çelik, Ufuk; Yurtay, Nilüfer; Koç, Emine Rabia; Tepe, Nermin; Güllüoğlu, Halil; Ertaş, Mustafa

    2015-01-01

    The present study evaluated the diagnostic accuracy of immune system algorithms with the aim of classifying the primary types of headache that are not related to any organic etiology. They are divided into four types: migraine, tension, cluster, and other primary headaches. After we took this main objective into consideration, three different neurologists were required to fill in the medical records of 850 patients into our web-based expert system hosted on our project web site. In the evaluation process, Artificial Immune Systems (AIS) were used as the classification algorithms. The AIS are classification algorithms that are inspired by the biological immune system mechanism that involves significant and distinct capabilities. These algorithms simulate the specialties of the immune system such as discrimination, learning, and the memorizing process in order to be used for classification, optimization, or pattern recognition. According to the results, the accuracy level of the classifier used in this study reache