Feature Selection Based on Ant Colony Optimization and Rough Set Theory

被引：5
|
作者：
He, Ming [1 ]
机构：
[1] Beijing Univ Technol, Coll Comp Sci, Beijing, Peoples R China
来源：
ISCSCT 2008: INTERNATIONAL SYMPOSIUM ON COMPUTER SCIENCE AND COMPUTATIONAL TECHNOLOGY, VOL 1, PROCEEDINGS | 2008年
关键词：
rough set; ant colony optimization; feature selection; core;
D O I：
10.1109/ISCSCT.2008.43
中图分类号：
TP301 [理论、方法];
学科分类号：
081202 ;
摘要：
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.
引用
收藏
页码：247 / 250
页数：4
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