Feature ranking in rough sets

被引：13

作者：

Hu, KY ^{[1
]}

Lu, YC ^{[1
]}

Shi, CY ^{[1
]}

机构：

[1] Tsinghua Univ, Dept Comp Sci, Beijing 100084, Peoples R China

来源：

AI COMMUNICATIONS | 2003年 / 16卷 / 01期

关键词：

rough set; reduct; discernibility matrix; data mining;

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：

The paper proposes a novel feature ranking technique using discernibility matrix. Discernibility matrix is used in rough set theory for reduct computation. By making use of attribute frequency information in discernibility matrix, the paper develops a fast feature ranking mechanism. Based on the mechanism, two heuristic reduct computation algorithms are proposed. One is for optimal reduct and the other for approximate reduct. Empirical results are also reported.

引用

页码：41 / 50

页数：10

共 14 条

[1]

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[2]

Deogun J., 1998, J ASIS, V5, P403

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Guan, JW ;

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ARTIFICIAL INTELLIGENCE, 1998, 105 (1-2) :77-103

[4]

Hu X., 1995, THESIS REGINA U

[5]

KOHAVI R, 1994, PROC INT C TOOLS ART, P740, DOI 10.1109/TAI.1994.346412

[6]

KRYSZKIEWICZ M, 1994, P INT WORKSH ROUGH S, P261

[7]

Liu H., 1998, FEATURE EXTRACTION C, DOI 10.1007/978-1-4615-5725-8

[8]

Merz C.J., UCI REPOSITORY MACHI

[9]

Michal G., 1994, RSL ROUGH SET LIB VE

[10]

Nguyen HS, 1999, LECT NOTES ARTIF INT, V1711, P137

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