Fuzzy Data Mining by Heuristic Rule Extraction and Multiobjective Genetic Rule Selection

In this paper, we demonstrate that multiobjective genetic rule selection can significantly improve the accuracy-complexity tradeoff curve of fuzzy rule-based classification systems generated by a heuristic rule extraction procedure for classification problems with many continuous attributes. First a prespecifled number of fuzzy rules are extracted in a heuristic manner based on a rule evaluation criterion. This step can be viewed as fuzzy data mining. Then multiobjective genetic rule selection is applied to the extracted rules to find a number of non-dominated rule sets with respect to accuracy maximization and complexity minimization. This step can be viewed as a postprocessing procedure in fuzzy data mining. Experimental results show that multiobjective genetic rule selection finds a number of smaller rule sets with higher classification accuracy than heuristically extracted rule sets. That is, the accuracy-complexity tradeoff curve of heuristically extracted rule sets in fuzzy data mining is improved by multiobjective genetic rule selection. This observation suggests that multiobjective genetic rule selection plays an important role in fuzzy data mining as a postprocessing procedure.