Title :
Feature Selection Based on Ant Colony Optimization and Rough Set Theory
Author_Institution :
Coll. of Comput. Sci., Beijing Univ. of Technol., Beijing, China
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;
Conference_Titel :
Computer Science and Computational Technology, 2008. ISCSCT '08. International Symposium on
Conference_Location :
Shanghai
Print_ISBN :
978-1-4244-3746-7
DOI :
10.1109/ISCSCT.2008.43
