DocumentCode
2285090
Title
Research on Feature Selection Algorithm Based on Mixed Model
Author
He, Ming
Author_Institution
Coll. of Comput. Sci., Beijing Univ. of Technol., Beijing
fYear
2008
fDate
20-22 Dec. 2008
Firstpage
70
Lastpage
72
Abstract
Reduct finding, especially optimal reduct finding, similar to feature selection problem, is a crucial task in rough set applications to data mining. In this paper, we have studied the basic concepts of rough set theory, and discussed several special cases of the ant colony optimization metaheuristic algorithms. Based on the above study, we propose a feature selection algorithm within a mixed framework based on rough set theory and ant colony optimization. experimental results show that the algorithm of this paper is flexible for feature selection.
Keywords
data mining; feature extraction; rough set theory; ant colony optimization metaheuristic algorithms; data mining; feature selection algorithm; mixed model; rough set theory; Ant colony optimization; Computer science; Data mining; Educational institutions; Filters; Helium; Information systems; Pattern recognition; Search methods; Set theory; ant colony optimization; feature selection; rough set;
fLanguage
English
Publisher
ieee
Conference_Titel
Computer and Electrical Engineering, 2008. ICCEE 2008. International Conference on
Conference_Location
Phuket
Print_ISBN
978-0-7695-3504-3
Type
conf
DOI
10.1109/ICCEE.2008.38
Filename
4740948
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