@inproceedings{205,
  author = {María Dolores Pérez Godoy and Antonio Jesús Rivera Rivas and Alberto Luis Fernández Hilario and Maria José del Jesus Díaz and Francisco Herrera Triguero},
  editor = {Joan Cabestany and Francisco Sandoval and Alberto Prieto and Juan Corchado},
  title = {A Preliminar Analysis of CO2RBFN in Imbalanced Problems},
  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.},
  year = {2009},
  pages = {57-64},
  publisher = {Springer Berlin Heidelberg},
  address = {Berlin, Heidelberg},
  isbn = {978-3-642-02478-8},
}