@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.}
}
@conference {10.1007/978-3-540-85863-8_79,
	title = {A Symbiotic CHC Co-evolutionary Algorithm for Automatic RBF Neural Networks Design},
	booktitle = {International Symposium on Distributed Computing and Artificial Intelligence 2008 (DCAI 2008)},
	year = {2009},
	pages = {663{\textendash}671},
	publisher = {Springer Berlin Heidelberg},
	organization = {Springer Berlin Heidelberg},
	address = {Berlin, Heidelberg},
	abstract = {This paper introduces Symbiotic{\_}CHC{\_}RBF, a co-evolutionary algorithm intended to automatically establish the parameters needed to design models for classification problems. Co-evolution involves two populations, which evolve together by means of a symbiotic relationship. One of the populations is the method EvRBF, which provides the design of radial basis function neural nets by means of evolutionary algorithms. The second population evolves sets of parameters for the method EvRBF, being every individual of the population a configuration of parameters for the method. Results show that Symbiotic{\_}CHC{\_}RBF can be effectively used to obtain good models, while reducing significantly the number of parameters to be fixed by hand.},
	isbn = {978-3-540-85863-8},
	author = {E. Parras-Guti{\'e}rrez and M. J. del Jesus and Merelo, Juan J. and Rivas, Victor M.},
	editor = {Corchado, Juan M. and Rodr{\'\i}guez, Sara and Llinas, James and Molina, Jos{\'e} M.}
}
