Diversity Role in Designing Multiple Classifier Systems Using MATLAB: Designing of MCS: A Diversity Approach - Couverture souple

Ahmed Khfagy, Muhammad Atta Othman

9783659522406: Diversity Role in Designing Multiple Classifier Systems Using MATLAB: Designing of MCS: A Diversity Approach

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ISBN 10 : 3659522406 ISBN 13 : 9783659522406

Editeur : LAP LAMBERT Academic Publishing, 2020

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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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�diteur: LAP LAMBERT Academic Publishing
Date d'�dition: 2020
Langue: anglais
ISBN 10: 3659522406
ISBN 13: 9783659522406
Reliure: Broch�
Nombre de pages: 104
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Diversity Role in Designing Multiple Classifier Systems Using MATLAB

Muhammad Atta Othman Ahmed Khfagy

Edit� par LAP LAMBERT Academic Publishing Mrz 2020, 2020

ISBN 10 : 3659522406 ISBN 13 : 9783659522406

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Taschenbuch. Etat : 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. N� de r�f. du vendeur 9783659522406

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Diversity Role in Designing Multiple Classifier Systems Using MATLAB

Muhammad Atta Othman Ahmed Khfagy

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Etat : 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. N� de r�f. du vendeur 385767260

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Diversity Role in Designing Multiple Classifier Systems Using MATLAB

Muhammad Atta Othman Ahmed Khfagy

Edit� par LAP LAMBERT Academic Publishing M�r 2020, 2020

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Taschenbuch. Etat : 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 Landstra�e 99, 66123 Saarbr�cken 104 pp. Englisch. N� de r�f. du vendeur 9783659522406

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Diversity Role in Designing Multiple Classifier Systems Using MATLAB : Designing of MCS: A Diversity Approach

Muhammad Atta Othman Ahmed Khfagy

Edit� par LAP LAMBERT Academic Publishing, 2020

ISBN 10 : 3659522406 ISBN 13 : 9783659522406

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Taschenbuch. Etat : 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. N� de r�f. du vendeur 9783659522406

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