Feature subset evaluation using fuzzy measures

Author

Chakraborty, B. ; Sawada, Y.

Author_Institution

Res. Inst. of Electr. Commun., Tohoku Univ., Sendai, Japan

fYear

1995

fDate

35030

Firstpage

220

Lastpage

225

Abstract

Feature selection is an important prerequisite of any pattern recognition system. For the selection of good features, one has to use some criterion for the assessment of its quality. Generally, a subset of M features are needed to be selected from all possible combinations of M features out of N features. In this paper, a measure for the evaluation of the effectiveness of a feature subset has been proposed with the help of fuzzy measures as an alternative to statistical measures. This measure, in conjunction with the branch-and-bound technique, can be used to find out the best possible feature subset from all possible subsets. The algorithm has been implemented on different data sets to explain its capability. The proposed measure is computationally easy and is suitable for use in a near-optimal search technique

Keywords

feature extraction; fuzzy set theory; tree searching; branch-and-bound technique; data sets; feature quality assessment criterion; feature selection; feature subset evaluation; fuzzy measures; near-optimal search technique; pattern recognition system; Character recognition; Distributed computing; Entropy; Fuzzy sets; Hamming distance; Pattern recognition; Power measurement; Redundancy; Size measurement; Time measurement;

fLanguage

English

Publisher

ieee

Conference_Titel

Intelligent Information Systems, 1995. ANZIIS-95. Proceedings of the Third Australian and New Zealand Conference on

Conference_Location

Perth, WA

Print_ISBN

0-86422-430-3

Type

conf

DOI

10.1109/ANZIIS.1995.705744

Filename

705744