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

Authors:
Susan E. Bedingfield;Kate A. Smith
Affiliations:
School of Business Systems, Monash University Clayton, Victoria, Australia;School of Business Systems, Monash University Clayton, Victoria, Australia
Venue:
ICCS'03 Proceedings of the 2003 international conference on Computational science
Year:
2003

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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.