Imbalanced Training Set Reduction and Feature Selection Through Genetic Optimization

Authors:
R. Barandela;J. K. Hernández;J. S. Sánchez;F. J. Ferri
Affiliations:
Instituto Tecnológico de Toluca, Av. Tecnológico s/n, 52140 Metepec, México;Instituto Tecnológico de Toluca, Av. Tecnológico s/n, 52140 Metepec, México;Dept. Llenguatges i Sistemes Informàtics, Universitat Jaume I, 12071 Castelló, Spain;Dept. d'Informàtica, Universitat de València, 46100 Burjassot (València), Spain
Venue:
Proceedings of the 2005 conference on Artificial Intelligence Research and Development
Year:
2005

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Abstract

Despite its simplicity and good classification performance, the Nearest Neighbor (NN) rule is not applied in many practical tasks because of the high amount of computational resources that it requires. Besides, when working with imbalanced training samples, its classification accuracy can be seriously degraded. In the present paper we propose two genetic algorithms to cope with these two issues. The purpose is to obtain complexity reduction while at the same time, to get a better balance in the training sample. Experimental results showing the benefits of our proposals are also reported.