Developments on a multi-objective metaheuristic (MOMH) algorithm for finding interesting sets of classification rules

Authors:
Beatriz de la Iglesia;Alan Reynolds;Vic J Rayward-Smith
Affiliations:
University of East Anglia, Norwich, Norfolk, UK;University of East Anglia, Norwich, Norfolk, UK;University of East Anglia, Norwich, Norfolk, UK
Venue:
EMO'05 Proceedings of the Third international conference on Evolutionary Multi-Criterion Optimization
Year:
2005

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Abstract

In this paper, we experiment with a combination of innovative approaches to rule induction to encourage the production of interesting sets of classification rules. These include multi-objective metaheuristics to induce the rules; measures of rule dissimilarity to encourage the production of dissimilar rules; and rule clustering algorithms to evaluate the results obtained. Our previous implementation of NSGA-II for rule induction produces a set of cc-optimal rules (coverage-confidence optimal rules). Among the set of rules produced there may be rules that are very similar. We explore the concept of rule similarity and experiment with a number of modifications of the crowding distance to increasing the diversity of the partial classification rules produced by the multi-objective algorithm.