Diversity Role in Designing Multiple Classifier Systems Using MATLAB - Ahmed Khfagy, Muhammad Atta Othman
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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…mehr

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Produktbeschreibung
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.
Autorenporträt
Dr. 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 Sohag University, Egypt. His main research interests are: Machine Learning, Artificial Intelligence, Biometrics and Information Security.