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

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.

► Hybrid neurologistic models based on Generalized Radial Basis Function.
► A novelty multinomial logistic regression method.
► Evaluation in a permanent disability classification problem.
► Useful information could be extracted from the most accurate model.