Permanent disability classification by combining evolutionary Generalized Radial Basis Function and logistic regression methods

Authors:
A. Castaño;Francisco Fernández-Navarro;P. A. Gutiérrez;César Hervás-Martínez
Affiliations:
Department of Informatics, University of Pinar del Río, Cuba;Department of Computer Science and Numerical Analysis, University of Córdoba, Campus de Rabanales, Albert Einstein Building, 3rd Floor, 14071 Córdoba, Spain;Department of Computer Science and Numerical Analysis, University of Córdoba, Campus de Rabanales, Albert Einstein Building, 3rd Floor, 14071 Córdoba, Spain;Department of Computer Science and Numerical Analysis, University of Córdoba, Campus de Rabanales, Albert Einstein Building, 3rd Floor, 14071 Córdoba, Spain
Venue:
Expert Systems with Applications: An International Journal
Year:
2012

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Abstract

Recently, a novelty multinomial logistic regression method where the initial covariate space is increased by adding the nonlinear transformations of the input variables given by Gaussian Radial Basis Functions (RBFs) obtained by an evolutionary algorithm was proposed. However, there still exist some problems with the standard Gaussian RBF, for example, the approximation of constant valued functions or the approximation of high dimensionality associated to some real problems. In order to face these problems, we propose the use of the generalized Gaussian RBF (GRBF) instead of the standard Gaussian RBF. Our approach has been validated with a real problem of disability classification, to evaluate its effectiveness. Experimental results show that this approach is able to achieve good generalization performance.