Knowledge extraction from radial basis function networks and multilayer perceptrons

Abstract

This paper deals with an evaluation and comparison of the accuracy and complexity of symbolic rules extracted from radial basis function networks and multilayer perceptrons. Here we examine the ability of rule extraction algorithms to extract meaningful rules that describe the overall performance of a particular network. In addition, the paper also highlights the suitability of a specific neural network architecture for particular classification problems. The study carried out on the extracted rule quality and complexity also has a direct bearing on the use of rule extraction algorithms for data mining and knowledge discovery.