A Discussion of Attribute Reduction in Fuzzy Rough Sets Using Support Vector Machine

Author

Tsang, Eric C C ; Chen, Degang ; Zhao, Suyun ; He, Qiang

Author_Institution

Hong Kong Polytech. Univ., Kowloon

Volume

4

fYear

2006

fDate

8-11 Oct. 2006

Firstpage

3436

Lastpage

3440

Abstract

This paper mainly focuses on the attribute reduction in fuzzy rough sets. An algorithm using discernibility matrix to compute all the attribute reductions is developed. After reducing the attributes, we introduce Support Vector Machine (SVM) as a classification technique to test the knowledge representation ability of attribute reduction. The numerical results show that the attribute reduction with fuzzy rough sets contains the same information as the original one.

Keywords

data analysis; data reduction; fuzzy set theory; knowledge representation; matrix algebra; pattern classification; rough set theory; support vector machines; SVM; attribute reduction; data analysis; discernibility matrix; fuzzy rough set; knowledge representation; pattern classification; support vector machine; Cybernetics; Fuzzy sets; Helium; Knowledge representation; Mathematics; Rough sets; Set theory; Support vector machine classification; Support vector machines; Testing;

fLanguage

English

Publisher

ieee

Conference_Titel

Systems, Man and Cybernetics, 2006. SMC '06. IEEE International Conference on

Conference_Location

Taipei

Print_ISBN

1-4244-0099-6

Electronic_ISBN

1-4244-0100-3

Type

conf

DOI

10.1109/ICSMC.2006.384650

Filename

4274414