@conference {10.1007/978-3-642-02478-8_8,
	title = {A Preliminar Analysis of CO2RBFN in Imbalanced Problems},
	booktitle = {Bio-Inspired Systems: Computational and Ambient Intelligence},
	year = {2009},
	pages = {57{\textendash}64},
	publisher = {Springer Berlin Heidelberg},
	organization = {Springer Berlin Heidelberg},
	address = {Berlin, Heidelberg},
	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 {\textquoteleft}{\textquoteleft}Synthetic Minority Over-sampling Technique{\textquoteright}{\textquoteright} (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.},
	isbn = {978-3-642-02478-8},
	author = {M.D. P{\'e}rez-Godoy and A.J. Rivera-Rivas and Fern{\'a}ndez, A. and M. J. del Jesus and F. Herrera},
	editor = {Cabestany, Joan and Sandoval, Francisco and Prieto, Alberto and Corchado, Juan M.}
}
