Data-based fuzzy rules extraction method for classification

In this study, a two-stage method which extracts fuzzy rules directly from samples is proposed for classification. First, we introduce a neighborhood based attribute significance algorithm to select r of the most important attributes from the original attribute set. Second, the proposed algorithm generates fuzzy rule from each sample described by the selected attribute subset and finally simplifies the returned fuzzy rule-base. A confidence degree is assigned for each of the extracted fuzzy rules by counting the number of training samples covered by the rule to solve the conflicts among the rules and then the rule-base is pruned. The performance of the proposed classification method have been compared with other five classification approaches including C4.5, DTable, OneR, NNge, and PART on seven UCI data sets. The experimental results show that the proposed method is better than other methods in two aspects: the higher classification accuracy and the smaller rule-base.