Evolutionary rule generation classification and its application to multi-class data

Abstract

This paper considers an evolutionary algorithm based on an information system for generating classification rules. Custom genetic operators and a multi-objective fitness function are designed for this representation. The approach has previously been illustrated using a binary class data set. In this paper we explore the possibility of using the algorithm on a multi-class data set. The accuracy of the rules produced by the evolutionary algorithm approach are compared to those obtained by a decision tree technique on the same data. The advantages of using an evolutionary classification technique over the more traditional decision tree structure are discussed. © Springer-Verlag Berlin Heidelberg 2003.