DocumentCode
2126685
Title
A Rough Set Based Hybrid Method to Feature Selection
Author
Ming, He
Author_Institution
Coll. of Comput. Sci., Beijing Univ. of Technol., Beijing
fYear
2008
fDate
21-22 Dec. 2008
Firstpage
585
Lastpage
588
Abstract
Features selection is a process to find the optimal subset of features that satisfy certain criteria. The aim of feature selection is to remove unnecessary features to the target concept. This paper investigates some basic concepts of rough set theory and ant colony optimization. Based on these studies, a hybrid approach to feature selection on combination of ant colony optimization and rough set theory is proposed. Experimental results obtained show this hybrid approach is a promising method for feature selection.
Keywords
feature extraction; optimisation; rough set theory; ant colony optimization; feature selection; rough set based hybrid method; Ant colony optimization; Computational modeling; Computer science; Educational institutions; Helium; Information systems; Knowledge acquisition; Machine learning; Optimization methods; Set theory;
fLanguage
English
Publisher
ieee
Conference_Titel
Knowledge Acquisition and Modeling, 2008. KAM '08. International Symposium on
Conference_Location
Wuhan
Print_ISBN
978-0-7695-3488-6
Type
conf
DOI
10.1109/KAM.2008.12
Filename
4732893
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