The Experts below are selected from a list of 310977 Experts worldwide ranked by ideXlab platform
Chiehjen Wang - One of the best experts on this subject based on the ideXlab platform.
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a ga based feature selection and parameters optimizationfor support vector machines
Expert Systems With Applications, 2006Co-Authors: Chenglung Huang, Chiehjen WangAbstract:Support Vector Machines, one of the new techniques for pattern Classification, have been widely used in many application areas. The kernel parameters setting for SVM in a training process impacts on the Classification Accuracy. Feature selection is another factor that impacts Classification Accuracy. The objective of this research is to simultaneously optimize the parameters and feature subset without degrading the SVM Classification Accuracy. We present a genetic algorithm approach for feature selection and parameters optimization to solve this kind of problem. We tried several real-world datasets using the proposed GA-based approach and the Grid algorithm, a traditional method of performing parameters searching. Compared with the Grid algorithm, our proposed GA-based approach significantly improves the Classification Accuracy and has fewer input features for support vector machines. q 2005 Elsevier Ltd. All rights reserved.
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a ga based feature selection and parameters optimizationfor support vector machines
Expert Systems With Applications, 2006Co-Authors: Chenglung Huang, Chiehjen WangAbstract:Support Vector Machines, one of the new techniques for pattern Classification, have been widely used in many application areas. The kernel parameters setting for SVM in a training process impacts on the Classification Accuracy. Feature selection is another factor that impacts Classification Accuracy. The objective of this research is to simultaneously optimize the parameters and feature subset without degrading the SVM Classification Accuracy. We present a genetic algorithm approach for feature selection and parameters optimization to solve this kind of problem. We tried several real-world datasets using the proposed GA-based approach and the Grid algorithm, a traditional method of performing parameters searching. Compared with the Grid algorithm, our proposed GA-based approach significantly improves the Classification Accuracy and has fewer input features for support vector machines. q 2005 Elsevier Ltd. All rights reserved.
Benjamin D Kaehler - One of the best experts on this subject based on the ideXlab platform.
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species abundance information improves sequence taxonomy Classification Accuracy
Nature Communications, 2019Co-Authors: Benjamin D Kaehler, Nicholas A Bokulich, Rob Knight, Daniel Mcdonald, Gregory J Caporaso, Gavin A HuttleyAbstract:Popular naive Bayes taxonomic classifiers for amplicon sequences assume that all species in the reference database are equally likely to be observed. We demonstrate that Classification Accuracy degrades linearly with the degree to which that assumption is violated, and in practice it is always violated. By incorporating environment-specific taxonomic abundance information, we demonstrate a significant increase in the species-level Classification Accuracy across common sample types. At the species level, overall average error rates decline from 25% to 14%, which is favourably comparable to the error rates that existing classifiers achieve at the genus level (16%). Our findings indicate that for most practical purposes, the assumption that reference species are equally likely to be observed is untenable. q2-clawback provides a straightforward alternative for samples from common environments. Taxonomy Classification of amplicon sequences is an important step in investigating microbial communities in microbiome analysis. Here, the authors show incorporating environment-specific taxonomic abundance information can lead to improved species-level Classification Accuracy across common sample types.
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species abundance information improves sequence taxonomy Classification Accuracy
bioRxiv, 2019Co-Authors: Benjamin D Kaehler, Nicholas A Bokulich, Rob Knight, Daniel Mcdonald, Gregory J Caporaso, Gavin A HuttleyAbstract:Popular naive Bayes taxonomic classifiers for amplicon sequences assume that all species in the reference database are equally likely to be observed. We demonstrate that Classification Accuracy degrades linearly with the degree to which that assumption is violated, and in practice it is always violated. By incorporating environment-specific taxonomic abundance information, we demonstrate that species-level resolution is attainable.
Gavin A Huttley - One of the best experts on this subject based on the ideXlab platform.
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species abundance information improves sequence taxonomy Classification Accuracy
Nature Communications, 2019Co-Authors: Benjamin D Kaehler, Nicholas A Bokulich, Rob Knight, Daniel Mcdonald, Gregory J Caporaso, Gavin A HuttleyAbstract:Popular naive Bayes taxonomic classifiers for amplicon sequences assume that all species in the reference database are equally likely to be observed. We demonstrate that Classification Accuracy degrades linearly with the degree to which that assumption is violated, and in practice it is always violated. By incorporating environment-specific taxonomic abundance information, we demonstrate a significant increase in the species-level Classification Accuracy across common sample types. At the species level, overall average error rates decline from 25% to 14%, which is favourably comparable to the error rates that existing classifiers achieve at the genus level (16%). Our findings indicate that for most practical purposes, the assumption that reference species are equally likely to be observed is untenable. q2-clawback provides a straightforward alternative for samples from common environments. Taxonomy Classification of amplicon sequences is an important step in investigating microbial communities in microbiome analysis. Here, the authors show incorporating environment-specific taxonomic abundance information can lead to improved species-level Classification Accuracy across common sample types.
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species abundance information improves sequence taxonomy Classification Accuracy
bioRxiv, 2019Co-Authors: Benjamin D Kaehler, Nicholas A Bokulich, Rob Knight, Daniel Mcdonald, Gregory J Caporaso, Gavin A HuttleyAbstract:Popular naive Bayes taxonomic classifiers for amplicon sequences assume that all species in the reference database are equally likely to be observed. We demonstrate that Classification Accuracy degrades linearly with the degree to which that assumption is violated, and in practice it is always violated. By incorporating environment-specific taxonomic abundance information, we demonstrate that species-level resolution is attainable.
Chenglung Huang - One of the best experts on this subject based on the ideXlab platform.
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a ga based feature selection and parameters optimizationfor support vector machines
Expert Systems With Applications, 2006Co-Authors: Chenglung Huang, Chiehjen WangAbstract:Support Vector Machines, one of the new techniques for pattern Classification, have been widely used in many application areas. The kernel parameters setting for SVM in a training process impacts on the Classification Accuracy. Feature selection is another factor that impacts Classification Accuracy. The objective of this research is to simultaneously optimize the parameters and feature subset without degrading the SVM Classification Accuracy. We present a genetic algorithm approach for feature selection and parameters optimization to solve this kind of problem. We tried several real-world datasets using the proposed GA-based approach and the Grid algorithm, a traditional method of performing parameters searching. Compared with the Grid algorithm, our proposed GA-based approach significantly improves the Classification Accuracy and has fewer input features for support vector machines. q 2005 Elsevier Ltd. All rights reserved.
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a ga based feature selection and parameters optimizationfor support vector machines
Expert Systems With Applications, 2006Co-Authors: Chenglung Huang, Chiehjen WangAbstract:Support Vector Machines, one of the new techniques for pattern Classification, have been widely used in many application areas. The kernel parameters setting for SVM in a training process impacts on the Classification Accuracy. Feature selection is another factor that impacts Classification Accuracy. The objective of this research is to simultaneously optimize the parameters and feature subset without degrading the SVM Classification Accuracy. We present a genetic algorithm approach for feature selection and parameters optimization to solve this kind of problem. We tried several real-world datasets using the proposed GA-based approach and the Grid algorithm, a traditional method of performing parameters searching. Compared with the Grid algorithm, our proposed GA-based approach significantly improves the Classification Accuracy and has fewer input features for support vector machines. q 2005 Elsevier Ltd. All rights reserved.
Giles M. Foody - One of the best experts on this subject based on the ideXlab platform.
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sample size determination for image Classification Accuracy assessment and comparison
International Journal of Remote Sensing, 2009Co-Authors: Giles M. FoodyAbstract:Many factors influence the quality and value of a Classification Accuracy assessment and evaluation programme. This paper focuses on the size of the testing set(s) used with particular regard to the impacts on Accuracy assessment and comparison. Testing set size is important as the use of an inappropriately large or small sample could lead to limited and sometimes erroneous assessments of Accuracy and of differences in Accuracy. Here, some of the basic statistical principles of sample size determination are outlined, including a discussion of Type II errors and their control. The paper provides a discussion on some of the basic issues of sample size determination for Accuracy assessment and includes factors linked to Accuracy comparison. With the latter, the researcher should specify the effect size (minimum meaningful difference in Accuracy), significance level and power used in an analysis and ideally also fit confidence limits to derived estimates. This will help design a study and aid the use of appro...
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Classification Accuracy comparison hypothesis tests and the use of confidence intervals in evaluations of difference equivalence and non inferiority
Remote Sensing of Environment, 2009Co-Authors: Giles M. FoodyAbstract:The comparison of Classification Accuracy statements has generally been based upon tests of difference or inequality when other scenarios and approaches may be more appropriate. Procedures for evaluating two scenarios with interest focused on the similarity in Accuracy values, non-inferiority and equivalence, are outlined following a discussion of tests of difference (inequality). It is also suggested that the confidence interval of the difference in Classification Accuracy may be used as well as or instead of conventional hypothesis testing to reveal more information about the disparity in the Classification Accuracy values compared.
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harshness in image Classification Accuracy assessment
Journal of remote sensing, 2008Co-Authors: Giles M. FoodyAbstract:Thematic mapping via a Classification analysis is one of the most common applications of remote sensing. The Accuracy of image Classifications is, however, often viewed negatively. Here, it is suggested that the approach to the evaluation of image Classification Accuracy typically adopted in remote sensing may often be unfair, commonly being rather harsh and misleading. It is stressed that the widely used target Accuracy of 85% can be inappropriate and that the approach to Accuracy assessment adopted commonly in remote sensing is pessimistically biased. Moreover, the maps produced by other communities, which are often used unquestioningly, may have a low Accuracy if evaluated from the standard perspective adopted in remote sensing. A greater awareness of the problems encountered in Accuracy assessment may help ensure that perceptions of Classification Accuracy are realistic and reduce unfair criticism of thematic maps derived from remote sensing.
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thematic map comparison evaluating the statistical significance of differences in Classification Accuracy
Photogrammetric Engineering and Remote Sensing, 2004Co-Authors: Giles M. FoodyAbstract:The Accuracy of thematic maps derived by image Classification analyses is often compared in remote sensing studies. This comparison is typically achieved by a basic subjective assessment of the observed difference in Accuracy but should be undertaken in a statistically rigorous fashion. One approach for the evaluation of the statistical significance of a difference in map Accuracy that has been widely used in remote sensing research is based on the comparison of the kappa coefficient of agreement derived for each map. The conventional approach to the comparison of kappa coefficients assumes that the samples used in their calculation are independent, an assumption that is commonly unsatisfied because the same sample of ground data sites is often used for each map. Alternative methods to evaluate the statistical significance of differences in Accuracy are available for both related and independent samples. Approaches for map comparison based on the kappa coefficient and proportion of correctly allocated cases, the two most widely used metrics of thematic map Accuracy in remote sensing, are discussed. An example illustrates how Classifications based on the same sample of ground data sites may be compared rigorously and highlights the importance of distinguishing between one- and two-sided statistical tests in the comparison of Classification Accuracy statements.
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Status of land cover Classification Accuracy assessment
Remote Sensing of Environment, 2002Co-Authors: Giles M. FoodyAbstract:The production of thematic maps, such as those depicting land cover, using an image Classification is one of the most common applications of remote sensing. Considerable research has been directed at the various components of the mapping process, including the assessment of Accuracy. This paper briefly reviews the background and methods of Classification Accuracy assessment that are commonly used and recommended in the research literature. It is, however, evident that the research community does not universally adopt the approaches that are often recommended to it, perhaps a reflection of the problems associated with Accuracy assessment, and typically fails to achieve the Accuracy targets commonly specified. The community often tends to use, unquestioningly, techniques based on the confusion matrix for which the correct application and interpretation requires the satisfaction of often untenable assumptions (e.g., perfect coregistration of data sets) and the provision of rarely conveyed information (e.g., sampling design for ground data acquisition). Eight broad problem areas that currently limit the ability to appropriately assess, document, and use the Accuracy of thematic maps derived from remote sensing are explored. The implications of these problems are that it is unlikely that a single standardized method of Accuracy assessment and reporting can be identified, but some possible directions for future research that may facilitate Accuracy assessment are highlighted.