Optimization of fuzzy rules for classification using genetic algorithm

Authors:
Myung Won Kim;Joung Woo Ryu;Samkeun Kim;Joong Geun Lee
Affiliations:
School of Computing, Soongsil University, Seoul, Korea;School of Computing, Soongsil University, Seoul, Korea;Dept. of Computer Engineering, Hankyoung National University, Kyonggi-do, Korea;Finance/Service Bussiness Unit, LG CNS Co.,Ltd., Seoul, Korea
Venue:
PAKDD'03 Proceedings of the 7th Pacific-Asia conference on Advances in knowledge discovery and data mining
Year:
2003

Citing 5
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Abstract

In this paper, we propose an efficient fuzzy rule generation algorithm based on fuzzy decision tree for high accuracy and better comprehensibility. We combine the comprehensibility of rules generated based on decision tree such as ID3 and C4.5 and the expressive power of fuzzy sets for dealing with quantitative data. Particularly, fuzzy rules allow us to effectively classify patterns of non-axis-parallel decision boundaries, which are difficult to do using attribute-based classification methods. We also investigate the use of genetic algorithm to optimize fuzzy decision trees in accuracy and comprehensibility by determining an appropriate set of membership functions for quantitative data. We have experimented our algorithm with several benchmark test data including manually generated two-class patterns, the iris data, the Wisconsin breast cancer data, and the credit screening data. The experiment results show that our method is more efficient in performance and comprehensibility of rules compared with methods including C4.5 and FID (Fuzzy ID3).