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

被引：0

作者：

Wang, XZ ^{[1
]}

Lu, XY ^{[1
]}

Zhang, F ^{[1
]}

机构：

[1] Hebei Univ, Fac Math & Comp Sci, Machine Learning Ctr, Baoding 071002, Peoples R China

来源：

PROCEEDINGS OF THE 2004 INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND CYBERNETICS, VOLS 1-7 | 2004年

关键词：

feature subset; information entropy; fuzzy-valued feature; fuzzy extension matrix; similarity degree;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

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.

引用

页码：2032 / 2035

页数：4

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