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Erschienen in: Soft Computing 9/2010

01.07.2010 | Original Paper

CO2RBFN: an evolutionary cooperative–competitive RBFN design algorithm for classification problems

verfasst von: María D. Perez-Godoy, Antonio J. Rivera, Francisco J. Berlanga, María José Del Jesus

Erschienen in: Soft Computing | Ausgabe 9/2010

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Abstract

This paper presents a new evolutionary cooperative–competitive algorithm for the design of Radial Basis Function Networks (RBFNs) for classification problems. The algorithm, CO2RBFN, promotes a cooperative–competitive environment where each individual represents a radial basis function (RBF) and the entire population is responsible for the final solution. The proposal considers, in order to measure the credit assignment of an individual, three factors: contribution to the output of the complete RBFN, local error and overlapping. In addition, to decide the operators’ application probability over an RBF, the algorithm uses a Fuzzy Rule Based System. It must be highlighted that the evolutionary algorithm considers a distance measure which deals, without loss of information, with differences between nominal features which are very usual in classification problems. The precision and complexity of the network obtained by the algorithm are compared with those obtained by different soft computing methods through statistical tests. This study shows that CO2RBFN obtains RBFNs with an appropriate balance between accuracy and simplicity, outperforming the other methods considered.

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Metadaten
Titel
CO2RBFN: an evolutionary cooperative–competitive RBFN design algorithm for classification problems
verfasst von
María D. Perez-Godoy
Antonio J. Rivera
Francisco J. Berlanga
María José Del Jesus
Publikationsdatum
01.07.2010
Verlag
Springer-Verlag
Erschienen in
Soft Computing / Ausgabe 9/2010
Print ISSN: 1432-7643
Elektronische ISSN: 1433-7479
DOI
https://doi.org/10.1007/s00500-009-0488-z

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