A modified back-propagation algorithm to deal with severe two-class imbalance problems on neural networks

Authors:
R. Alejo;P. Toribio;R. M. Valdovinos;J. H. Pacheco-Sanchez
Affiliations:
Tecnológico de Estudios Superiores de Jocotitlán, Jocotitlán, Mexico;Tecnológico de Estudios Superiores de Jocotitlán, Jocotitlán, Mexico;Centro Universitario UAEM Valle de Chalco, Universidad Autónoma del Estado de México, Valle de Chalco, Mexico;Instituto Tecnológico de Toluca, Metepec, Mexico
Venue:
MCPR'12 Proceedings of the 4th Mexican conference on Pattern Recognition
Year:
2012

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Abstract

In this paper we propose a modified back-propagation to deal with severe two-class imbalance problems. The method consists in automatically to find the over-sampling rate to train a neural network (NN), i.e., identify the appropriate number of minority samples to train the NN during the learning stage, so to reduce training time. The experimental results show that the performance proposed method is a very competitive when it is compared with conventional SMOTE, and its training time is lesser.