An efficient fuzzy classifier with feature selection based on fuzzy entropy

This paper presents an efficient fuzzy classifier with the ability of Featu re selection based on a fuzzy entropy measure. Fuzzy entropy is employed to evaluate the information of pattern distribution in the pattern space. Wit h this information, we can partition the pattern space into nonoverlapping decision regions for pattern classification. Since the decision regions do not overlap, both the complexity and computational load of the classifier a re reduced and thus the training time and classification time are extremely short. Although the decision regions are partitioned into nonoverlapping s ubspaces, we can achieve good classification performance since the decision regions can be correctly determined via our proposed fuzzy entropy measure . In addition, we also investigate the use of fuzzy entropy to select relev ant features. The feature selection procedure not only reduces the dimensio nality of a problem but also discards noise-corrupted, redundant and unimpo rtant features. Finally, we apply the proposed classifier to the Iris datab ase and Wisconsin breast cancer database to evaluate the classification per formance. Both of the results show that the proposed classifier can work we ll for the pattern classification application.