Title :
Research on Feature Selection Algorithm Based on Mixed Model
Author_Institution :
Coll. of Comput. Sci., Beijing Univ. of Technol., Beijing
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;
Conference_Titel :
Computer and Electrical Engineering, 2008. ICCEE 2008. International Conference on
Conference_Location :
Phuket
Print_ISBN :
978-0-7695-3504-3
DOI :
10.1109/ICCEE.2008.38