Abstract
In this paper, we present a new integrated architecture and feature selection algorithm for radial basis neural networks (RBNNs). The objective is to apply the network in an iterative fashion to determine the final architecture and feature set used for the purpose of classification. Numerical experiments were conducted to compare the performance of the new algorithm to that of discriminant analysis (DA) and feed-forward neural networks (FFNNs) on four problems: the University of Wisconsin breast cancer data, a noise-corrupted version of Fisher's iris problem and two problems of higher dimensionality taken from UCI Repository of Machine Learning Databases and Domain Theories (http://www.mat.univie.ac.at/∼neum/contrib/class.data). Preliminary numerical results indicate great promise for the new algorithm as a general classification tool for a wide range of problems.
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