Diversity Role in Designing Multiple Classifier Systems Using MATLAB: Designing of MCS: A Diversity Approach - Softcover
Synopsis
Multiple Classifiers Systems (MCS) perform in formation fusion of classification decisions at different levels overcoming limitations of traditional approaches based on single classifiers. We address one of the main open issues about the use of Diversity in Multiple Classifier Systems: the effectiveness of the explicit use of diversity measures for creation of classifier ensembles. So far, diversity measures have been mostly used for ensemble pruning, namely, for selecting a subset of classifiers out of an original, larger ensemble. Here we focus on pruning techniques based on forward selection, since they allow a direct comparison with the simple estimation of accuracy of classifier ensemble. We empirically carry out this comparison for several diversity measures and bench mark data sets, using bagging as the ensemble construction technique, and majority voting as the fusion rule.
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Diversity Role in Designing Multiple Classifier Systems Using MATLAB
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LAP LAMBERT Academic Publishing Mrz 2020, 2020
ISBN 10: 3659522406
ISBN 13: 9783659522406
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Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Multiple Classifiers Systems (MCS) perform in formation fusion of classification decisions at different levels overcoming limitations of traditional approaches based on single classifiers. We address one of the main open issues about the use of Diversity in Multiple Classifier Systems: the effectiveness of the explicit use of diversity measures for creation of classifier ensembles. So far, diversity measures have been mostly used for ensemble pruning, namely, for selecting a subset of classifiers out of an original, larger ensemble. Here we focus on pruning techniques based on forward selection, since they allow a direct comparison with the simple estimation of accuracy of classifier ensemble. We empirically carry out this comparison for several diversity measures and bench mark data sets, using bagging as the ensemble construction technique, and majority voting as the fusion rule. 104 pp. Englisch. Seller Inventory # 9783659522406
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Diversity Role in Designing Multiple Classifier Systems Using MATLAB
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ISBN 10: 3659522406
ISBN 13: 9783659522406
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Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Ahmed Khfagy Muhammad Atta OthmanDr. Muhammad AOA Khfagy is a Lecturer of Computer Science. He received the PhD degree (2018) in Computer Engineering at the University of Cagliari, Italy. He awarded the MSc and the BSc degrees from S. Seller Inventory # 385767260
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Diversity Role in Designing Multiple Classifier Systems Using MATLAB
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LAP LAMBERT Academic Publishing Mär 2020, 2020
ISBN 10: 3659522406
ISBN 13: 9783659522406
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Taschenbuch. Condition: Neu. This item is printed on demand - Print on Demand Titel. Neuware -Multiple Classi¿ers Systems (MCS) perform in formation fusion of classi¿cation decisions at different levels overcoming limitations of traditional approaches based on single classi¿ers. We address one of the main open issues about the use of Diversity in Multiple Classi¿er Systems: the effectiveness of the explicit use of diversity measures for creation of classi¿er ensembles. So far, diversity measures have been mostly used for ensemble pruning, namely, for selecting a subset of classi¿ers out of an original, larger ensemble. Here we focus on pruning techniques based on forward selection, since they allow a direct comparison with the simple estimation of accuracy of classi¿er ensemble. We empirically carry out this comparison for several diversity measures and bench mark data sets, using bagging as the ensemble construction technique, and majority voting as the fusion rule.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 104 pp. Englisch. Seller Inventory # 9783659522406
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Diversity Role in Designing Multiple Classifier Systems Using MATLAB : Designing of MCS: A Diversity Approach
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Taschenbuch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Multiple Classifiers Systems (MCS) perform in formation fusion of classification decisions at different levels overcoming limitations of traditional approaches based on single classifiers. We address one of the main open issues about the use of Diversity in Multiple Classifier Systems: the effectiveness of the explicit use of diversity measures for creation of classifier ensembles. So far, diversity measures have been mostly used for ensemble pruning, namely, for selecting a subset of classifiers out of an original, larger ensemble. Here we focus on pruning techniques based on forward selection, since they allow a direct comparison with the simple estimation of accuracy of classifier ensemble. We empirically carry out this comparison for several diversity measures and bench mark data sets, using bagging as the ensemble construction technique, and majority voting as the fusion rule. Seller Inventory # 9783659522406
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Diversity Role in Designing Multiple Classifier Systems Using MATLAB | Designing of MCS: A Diversity Approach
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ISBN 13: 9783659522406
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Taschenbuch. Condition: Neu. Diversity Role in Designing Multiple Classifier Systems Using MATLAB | Designing of MCS: A Diversity Approach | Muhammad Atta Othman Ahmed Khfagy | Taschenbuch | 104 S. | Englisch | 2020 | LAP LAMBERT Academic Publishing | EAN 9783659522406 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu. Seller Inventory # 118208854
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Diversity Role in Designing Multiple Classifier Systems Using MATLAB: Designing of MCS: A Diversity Approach
Published by
LAP LAMBERT Academic Publishing, 2020
ISBN 10: 3659522406
ISBN 13: 9783659522406
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