Feature Selection Based on Ant Colony Optimization and Rough Set Theory

Author

He, Ming

Author_Institution

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

Volume

1

fYear

2008

fDate

20-22 Dec. 2008

Firstpage

247

Lastpage

250

Abstract

Ant colony optimization (ACO) algorithms have been applied successfully to combinatorial optimization problems. Rough set theory offers a viable approach for feature selection from data sets. In this paper, the basic concepts of rough set theory and ant colony optimization are introduced, and the role of the basic constructs of rough set approach in feature selection, namely attribute reduction is studied. Base above research, a rough set and ACO based algorithm for feature selection problems is proposed. Finally, the presented algorithm was tested on UCI data sets and performed effectively.

Keywords

combinatorial mathematics; data mining; data reduction; feature extraction; optimisation; rough set theory; ant colony optimization; attribute reduction; combinatorial optimization problem; feature selection; rough set theory; Ant colony optimization; Computer science; Data mining; Educational institutions; Helium; Information systems; Machine learning; Pattern recognition; Set theory; Testing; ant colony optimization; core; feature selection; rough set;

fLanguage

English

Publisher

ieee

Conference_Titel

Computer Science and Computational Technology, 2008. ISCSCT '08. International Symposium on

Conference_Location

Shanghai

Print_ISBN

978-1-4244-3746-7

Type

conf

DOI

10.1109/ISCSCT.2008.43

Filename

4731418