Radial basis function networks and decision trees in the determination of a classifier

In this paper a nonparametric classifier which combines radial basis function networks and binary classification trees is proposed. The joint use of the two methods may be preferable not only with respect to radial basis function networks, but also to recursive partitioning techniques, as it may help to integrate the knowledge acquired by the single classifiers. A simulation study, based on a two-class problem, shows that this method represents a valid solution, particularly in the presence of noise variables.