Improving the classification accuracy of RBF and MLP neural networks trained with imbalanced samples

Authors:
R. Alejo;V. Garcia;J. M. Sotoca;R. A. Mollineda;J. S. Sánchez
Affiliations:
Dept. Llenguatges i Sistemes Informàtics, Universitat Jaume I, Castelló de la Plana, Spain;Dept. Llenguatges i Sistemes Informàtics, Universitat Jaume I, Castelló de la Plana, Spain;Dept. Llenguatges i Sistemes Informàtics, Universitat Jaume I, Castelló de la Plana, Spain;Dept. Llenguatges i Sistemes Informàtics, Universitat Jaume I, Castelló de la Plana, Spain;Dept. Llenguatges i Sistemes Informàtics, Universitat Jaume I, Castelló de la Plana, Spain
Venue:
IDEAL'06 Proceedings of the 7th international conference on Intelligent Data Engineering and Automated Learning
Year:
2006

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Abstract

In practice, numerous applications exist where the data are imbalanced. It supposes a damage in the performance of the classifier. In this paper, an appropriate metric for imbalanced data is applied as a filtering technique in the context of Nearest Neighbor rule, to improve the classification accuracy in RBF and MLP neural networks. We diminish atypical or noisy patterns of the majority-class keeping all samples of the minority-class. Several experiments with these preprocessing techniques are performed in the context of RBF and MLP neural networks.