Evolutionary q-gaussian radial basis functions for binary-classification

Authors:
F. Fernández-Navarro;C. Hervás-Martínez;P. A. Gutiérrez;M. Cruz-Ramírez;M. Carbonero-Ruz
Affiliations:
Department of Computer Science and Numerical Analysis, University of Cordoba, Córdoba, Spain;Department of Computer Science and Numerical Analysis, University of Cordoba, Córdoba, Spain;Department of Computer Science and Numerical Analysis, University of Cordoba, Córdoba, Spain;Department of Computer Science and Numerical Analysis, University of Cordoba, Córdoba, Spain;Department of Management and Quantitative Methods, ETEA, Cordoba, Spain
Venue:
HAIS'10 Proceedings of the 5th international conference on Hybrid Artificial Intelligence Systems - Volume Part II
Year:
2010

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Abstract

This paper proposes a Radial Basis Function Neural Network (RBFNN) which reproduces different Radial Basis Functions (RBFs) by means a real parameter q, named q-Gaussian RBFNN The architecture, weights and node topology are learnt through a Hybrid Algorithm (HA) with the iRprop+ algorithm as the local improvement procedure In order to test its overall performance, an experimental study with eleven datasets, taken from the UCI repository is presented The RBFNN with the q-Gaussian is compared to RBFNN with Gaussian, Cauchy and Inverse Multiquadratic RBFs.