Predictive Subset Selection using Regression Trees and RBF Neural Networks Hybridized with the Genetic Algorithm

Oguz Akbilgic, Hamparsum Bozdogan


In this paper we develop a novel nonparametric predictive subset regression modeling procedure that involves a combination of regression trees with radial basis function (RBF) neural networks hybridized with the genetic algorithm (GA) to carry out the subset selection of the best predictors. We use the information-theoretic measure of complexity (ICOMP) criterion of [5, 6, 7, 8] as our fitness function to choose the best approximating radial basis functions and to choose the best subset of predictors with the GA. To avoid the potential singularities in the design matrix, we combine our model with analytical global ridge regression for regularization. On the other hand, estimation and prediction performance of model also taken into account for best subset chosen.


Model Selection, Subset Selection, Information Criteria, Radial Basis Functions, Neural Networks

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