Classification by evolutionary generalised radial basis functions

Authors:
Adiel Castañ/o;Francisco Ferná/ndez-Navarro;Cé/sar Hervá/s-Martí/nez;M. M. Garcí/a;Pedro Antonio Gutié/rrez
Affiliations:
(Correspd. Tel./Fax: +53 48726803/ E-mail: adiel@info.upr.edu.cu) Department of Informatics, University of Pinar del Rio, Pinar del Rio, Cuba;Department of Computer Science and Numerical Analysis, University of Có/rdoba, Campus de Rabanales, Albert Einstein building, 3rd floor, 14071 - Có/rdoba, Spain;Department of Computer Science and Numerical Analysis, University of Có/rdoba, Campus de Rabanales, Albert Einstein building, 3rd floor, 14071 - Có/rdoba, Spain;Department of Computer Science, University of Las Villas, Santa Clara, Cuba;Department of Computer Science and Numerical Analysis, University of Có/rdoba, Campus de Rabanales, Albert Einstein building, 3rd floor, 14071 - Có/rdoba, Spain
Venue:
International Journal of Hybrid Intelligent Systems - Advances in Intelligent Agent Systems
Year:
2010

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Abstract

This paper proposes a Neural Network model using Generalised kernel functions for the hidden layer of a feed forward network. These functions are Generalised Radial Basis Functions (GRBF), and the architecture, weights and node topology are learned through an evolutionary algorithm. The proposed model is compared with the corresponding standard hidden-node models: Product Unit (PU) neural networks, Multilayer Perceptrons (MLP) with Sigmoidal Units (SUs) and the RBF neural networks. The proposed methodology is tested using twelve benchmark classification datasets from well-known machine learning problems. GRBFs are found to perform better than other standard basis functions at the classification task.