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Titolo:
An efficient fuzzy classifier with feature selection based on fuzzy entropy
Autore:
Lee, HM; Chen, CM; Chen, JM; Jou, YL;
Indirizzi:
Natl Taiwan Univ Sci & Technol, Dept Elect Engn, Taipei, Taiwan Natl Taiwan Univ Sci & Technol Taipei Taiwan Elect Engn, Taipei, Taiwan Natl Taiwan Univ Sci & Technol, Inst Elect Engn, Taipei, Taiwan Natl Taiwan Univ Sci & Technol Taipei Taiwan Elect Engn, Taipei, Taiwan Natl Taiwan Univ Sci & Technol, INFOLIGHT Technol Corp, Taipei, Taiwan Natl Taiwan Univ Sci & Technol Taipei Taiwan chnol Corp, Taipei, Taiwan
Titolo Testata:
IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART B-CYBERNETICS
fascicolo: 3, volume: 31, anno: 2001,
pagine: 426 - 432
SICI:
1083-4419(200106)31:3<426:AEFCWF>2.0.ZU;2-9
Fonte:
ISI
Lingua:
ENG
Soggetto:
NEURAL-NETWORK CLASSIFIER; FEATURE SUBSET-SELECTION; GENETIC ALGORITHMS; FEATURE SPACE; RULES; DISCRETIZATION; REGIONS; SYSTEMS;
Keywords:
feature selection; fuzzy classifier; fuzzy entropy;
Tipo documento:
Article
Natura:
Periodico
Settore Disciplinare:
Engineering, Computing & Technology
Citazioni:
46
Recensione:
Indirizzi per estratti:
Indirizzo: Lee, HM Natl Taiwan Univ Sci & Technol, Dept Elect Engn, Taipei, Taiwan Natl Taiwan Univ Sci & Technol Taipei Taiwan gn, Taipei, Taiwan
Citazione:
H.M. Lee et al., "An efficient fuzzy classifier with feature selection based on fuzzy entropy", IEEE SYST B, 31(3), 2001, pp. 426-432

Abstract

This paper presents an efficient fuzzy classifier with the ability of Feature selection based on a fuzzy entropy measure. Fuzzy entropy is employed to evaluate the information of pattern distribution in the pattern space. With this information, we can partition the pattern space into nonoverlappingdecision regions for pattern classification. Since the decision regions donot overlap, both the complexity and computational load of the classifier are reduced and thus the training time and classification time are extremely short. Although the decision regions are partitioned into nonoverlapping subspaces, 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 relevant features. The feature selection procedure not only reduces the dimensionality of a problem but also discards noise-corrupted, redundant and unimportant features. Finally, we apply the proposed classifier to the Iris database and Wisconsin breast cancer database to evaluate the classification performance. Both of the results show that the proposed classifier can work well for the pattern classification application.

ASDD Area Sistemi Dipartimentali e Documentali, Università di Bologna, Catalogo delle riviste ed altri periodici
Documento generato il 07/04/20 alle ore 22:40:39