Comparison of heuristic criteria for fuzzy rule selection in classification problems

被引：2

作者：

Ishibuchi H. ^{[1
]}

Yamamoto T. ^{[1
]}

机构：

[1] Department of Industrial Engineering, Osaka Prefecture University, Sakai

来源：

Fuzzy Optimization and Decision Making | 2004年 / 3卷 / 2期

关键词：

Data mining; Fuzzy rules; Genetic algorithm; Pattern classification; Rule extraction; Rule selection;

D O I：

10.1023/B:FODM.0000022041.98349.12

中图分类号：

学科分类号：

摘要：

This paper compares heuristic criteria used for extracting a pre-specified number of fuzzy classification rules from numerical data. We examine the performance of each heuristic criterion through computational experiments on well-known test problems. Experimental results show that better results are obtained from composite criteria of confidence and support measures than their individual use. It is also shown that genetic algorithm-based rule selection can improve the classification ability of extracted fuzzy rules by searching for good rule combinations. This observation suggests the importance of taking into account the combinatorial effect of fuzzy rules (i.e., the interaction among them).

引用

页码：119 / 139

页数：20

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