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Heuristic extraction of fuzzy classification rules using data mining techniques: an empirical study on benchmark data sets

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2 Author(s)
Ishibuchi, H. ; Dept. of Ind. Eng., Osaka Prefecture Univ., Japan ; Yamamoto, T.

We examine the performance of compact fuzzy rule-based classification systems that consist of a small number of simple fuzzy rules with high comprehensibility. Those fuzzy systems are designed in a heuristic manner using rule selection criteria. We first describe fuzzy rule-based classification. Next we describe heuristic rule selection criteria using the terminology in data mining: confidence and support. A small number of fuzzy rules are extracted from numerical data based on each rule selection criterion. Then we examine the classification performance of extracted fuzzy rules through computational experiments on a number of benchmark data sets from the UCI ML Repository. Our results should be viewed as the lowest benchmark performance of fuzzy rule-based classification systems because fuzzy rules are extracted using a simple heuristic method with no optimization or tuning procedures. Nevertheless our results on some data sets are comparable to reported results by the C4.5 algorithm in the literature.

Published in:

Fuzzy Systems, 2004. Proceedings. 2004 IEEE International Conference on  (Volume:1 )

Date of Conference:

25-29 July 2004