Prescreening of Candidate Rules Using Association Rule Mining and Pareto-optimality in Genetic Rule Selection

Authors:
Hisao Ishibuchi;Isao Kuwajima;Yusuke Nojima
Affiliations:
Department of Computer Science and Intelligent Systems, Graduate School of Engineering, Osaka Prefecture University, 1-1 Gakuen-cho, Naka-ku, Sakai, Osaka 599-8531, Japan;Department of Computer Science and Intelligent Systems, Graduate School of Engineering, Osaka Prefecture University, 1-1 Gakuen-cho, Naka-ku, Sakai, Osaka 599-8531, Japan;Department of Computer Science and Intelligent Systems, Graduate School of Engineering, Osaka Prefecture University, 1-1 Gakuen-cho, Naka-ku, Sakai, Osaka 599-8531, Japan
Venue:
KES '07 Knowledge-Based Intelligent Information and Engineering Systems and the XVII Italian Workshop on Neural Networks on Proceedings of the 11th International Conference
Year:
2007

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Abstract

Genetic rule selection is an approach to the design of classifiers with high accuracy and high interpretability. It searches for a small number of simple classification rules from a large number of candidate rules. The effectiveness of genetic rule selection strongly depends on the choice of candidate rules. If we have hundreds of thousands of candidate rules, it is very difficult to efficiently search for their good subsets. On the other hand, if we have only a few candidate rules, rule selection does not make sense. In this paper, we examine the use of Pareto-optimal and near Pareto-optimal rules with respect to support and confidence as candidate rules in genetic rule selection.