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 ...
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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.
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Add this copy of Diversity Role in Designing Multiple Classifier Systems to cart. $96.34, good condition, Sold by Bonita rated 4.0 out of 5 stars, ships from Santa Clarita, CA, UNITED STATES, published 2020 by LAP LAMBERT Academic Publishin.
