A Preliminar Analysis of CO2RBFN in Imbalanced Problems

TitleA Preliminar Analysis of CO2RBFN in Imbalanced Problems
Publication TypeConference Paper
Year of Publication2009
AuthorsPérez-Godoy, M.D., Rivera-Rivas A.J., Fernández A., del Jesus M. J., and Herrera F.
EditorCabestany, Joan, Sandoval Francisco, Prieto Alberto, and Corchado Juan M.
Conference NameBio-Inspired Systems: Computational and Ambient Intelligence
PublisherSpringer Berlin Heidelberg
Conference LocationBerlin, Heidelberg
ISBN Number978-3-642-02478-8

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.