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