Journal article

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

SE Bedingfield, KA Smith, PMA Sloot (ed.), D Abramson (ed.), AV Bogdanov (ed.), JJ Dongarra (ed.), AY Zomaya (ed.), YE Gorbachev (ed.)

COMPUTATIONAL SCIENCE - ICCS 2003, PT IV, PROCEEDINGS | SPRINGER-VERLAG BERLIN | Published : 2003

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 Heidelber..

View full abstract

University of Melbourne Researchers