An efficient fuzzy classifier with feature selection based on fuzzy entropy

Citation
Hm. Lee et al., An efficient fuzzy classifier with feature selection based on fuzzy entropy, IEEE SYST B, 31(3), 2001, pp. 426-432
Citations number
46
Language
INGLESE
art.tipo
Article
Categorie Soggetti
AI Robotics and Automatic Control
Journal title
IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART B-CYBERNETICS
ISSN journal
1083-4419 → ACNP
Volume
31
Issue
3
Year of publication
2001
Pages
426 - 432
Database
ISI
SICI code
1083-4419(200106)31:3<426:AEFCWF>2.0.ZU;2-9
Abstract
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.