Feature selection based on fuzzy extension matrix for multi-class problem

Author

Wang, Xi-Zhao ; Lu, Xiao-Ying ; Zhang, Feng

Author_Institution

Machine Learning Center, Hebei Univ., Baoding, China

Volume

4

fYear

2004

fDate

26-29 Aug. 2004

Firstpage

2032

Abstract

Feature subset selection is one of the widely used and practical methods for pattern recognition and classification, which aims to reduce the number of features to be used. Optimal fuzzy-valued feature subset selection (OFFSS) method is efficient for feature subset selection of two-class problem. However, the original OFFSS is not suitable for multi-class problem. This paper gives an improved version of OFFSS. The OFFSS algorithm is extended to the multi-class problem in which information entropy is used to reduce computational complexity of the method. The feasibility and simplicity of the improved algorithm are demonstrated by applying it to fuzzy decision tree induction.

Keywords

computational complexity; decision trees; entropy; feature extraction; fuzzy set theory; matrix algebra; pattern classification; computational complexity; fuzzy decision tree induction; fuzzy extension matrix; information entropy; multiclass problem; optimal fuzzy-valued feature subset selection method; pattern classification; pattern recognition; Computational complexity; Computer science; Decision trees; Electronic mail; Heuristic algorithms; Induction generators; Information entropy; Machine learning; Mathematics; Pattern recognition;

fLanguage

English

Publisher

ieee

Conference_Titel

Machine Learning and Cybernetics, 2004. Proceedings of 2004 International Conference on

Print_ISBN

0-7803-8403-2

Type

conf

DOI

10.1109/ICMLC.2004.1382129

Filename

1382129