Genetic rule selection with a multi-classifier coding scheme for ensemble classifier design

Authors:
Yusuke Nojima;Hisao Ishibuchi
Affiliations:
(Correspd. Tel.: +81 72 254 9198/ Fax: +81 72 254 9915/ nojima@cs.osakafu-u.ac.jp) Dept. of Comp. Sci. and Intell. Sys., Grad. Sch. of Eng., Osaka Prefecture University, 1-1 Gakuen-cho, Naka-ku, S ...;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:
International Journal of Hybrid Intelligent Systems - Hybridization of Intelligent Systems
Year:
2007

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Abstract

In this paper, we examine the effectiveness of genetic rule selection with a multi-classifier coding scheme for ensemble classifier design. Genetic rule selection is a two-stage method. The first stage is rule extraction from numerical data using a data mining technique. Extracted rules are used as candidate rules. The second stage is evolutionary multiobjective rule selection from the candidate rules. We use a multi-classifier coding scheme where an ensemble classifier is represented by an integer string. Three criteria are used as objective functions in evolutionary multiobjective rule selection to optimize ensemble classifiers in terms of accuracy and diversity. We examine the performance of designed ensemble classifiers through computational experiments on six benchmark datasets in the UCI machine learning repository.