Feature Selection Based on Neighborhood Systems and Rough Set Theory

Author

He, Ming

Author_Institution

Coll. of Comput. Sci., Beijing Univ. of Technol. Beijing, Beijing

fYear

2009

fDate

23-25 Jan. 2009

Firstpage

3

Lastpage

5

Abstract

Attribute reduction is an important issue in data mining and knowledge acquisition. It has been proven that computing all reductions and optimal (minimal) reduction is a NP-hard problem. This paper proposed a hybrid approach using the rough set theory and neighborhood systems for feature selection. Two neighborhood approximation operators are defined based on rough set. A neighborhood rough model is constructed subsequently and the heuristic information is introduced according to the significance of attributes respectively. Experimental results indicate that the proposed method can reduce attributes effectively.

Keywords

approximation theory; computational complexity; data mining; mathematical operators; optimisation; rough set theory; NP-hard problem; attribute reduction; data mining; feature selection; heuristic information; knowledge acquisition; neighborhood approximation operator; optimal reduction; rough set theory; Data analysis; Data mining; Educational institutions; Feature extraction; Helium; Information systems; Knowledge acquisition; Machine learning; NP-hard problem; Set theory; feature selection; neighborhood systems; rough set;

fLanguage

English

Publisher

ieee

Conference_Titel

Knowledge Discovery and Data Mining, 2009. WKDD 2009. Second International Workshop on

Conference_Location

Moscow

Print_ISBN

978-0-7695-3543-2

Type

conf

DOI

10.1109/WKDD.2009.11

Filename

4771864