A Preliminar Analysis of CO2RBFN in Imbalanced Problems

Authors:
M. D. Pérez-Godoy;A. J. Rivera;A. Fernández;M. J. Jesus;F. Herrera
Affiliations:
Department of Computer Science, University of Jaén, Jaén, Spain 23071;Department of Computer Science, University of Jaén, Jaén, Spain 23071;Department of Computer Science and Artificial Intelligence, University of Granada, Granada, Spain 18071;Department of Computer Science, University of Jaén, Jaén, Spain 23071;Department of Computer Science and Artificial Intelligence, University of Granada, Granada, Spain 18071
Venue:
IWANN '09 Proceedings of the 10th International Work-Conference on Artificial Neural Networks: Part I: Bio-Inspired Systems: Computational and Ambient Intelligence
Year:
2009

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Abstract

In many real classification problems the data are imbalanced, i.e., the number of instances for some classes are much higher than that of the other classes. Solving a classification task using such an imbalanced data-set is difficult due to the bias of the training towards the majority classes. The aim of this contribution is to analyse the performance of CO2RBFN, a cooperative-competitive evolutionary model for the design of RBFNs applied to classification problems on imbalanced domains and to study the cooperation of a well known preprocessing method, the "Synthetic Minority Over-sampling Technique" (SMOTE) with our algorithm. The good performance of CO2RBFN is shown through an experimental study carried out over a large collection of imbalanced data-sets.