Experimental comparison of feature subset selection using GA and ACO algorithm

被引:0
作者
Lee, Keunjoon
Joo, Jinu
Yang, Jihoon
Honavar, Vasant
机构
[1] LG Elect Inc, Dev Lab 1, Mobile Handset R&D Ctr, Mobile Commun Co, Seoul 153801, South Korea
[2] Sejong Daewoo BD, Seoul 110070, South Korea
[3] Sogang Univ, Dept Comp Sci, Seoul 121742, South Korea
[4] Iowa State Univ, Dept Comp Sci, Artificial Intelligence Res Lab, Ames, IA 50011 USA
来源
ADVANCED DATA MINING AND APPLICATIONS, PROCEEDINGS | 2006年 / 4093卷
关键词
D O I
暂无
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Practical pattern classification and knowledge discovery problems require selecting a useful subset of features from a much larger set to represent the patterns to be classified. Exhaustive evaluation of possible feature subsets is usually infeasible in practice because of the large amount of computational effort required. Bio-inspired algorithms offer an attractive approach to find near-optimal solutions to such optimization problems. This paper presents an approach to feature subset selection using bio-inspired algorithms. Our experiments with several benchmark real-world pattern classification problems demonstrate the feasibility of this approach to feature subset selection in the automated design of neural networks for pattern classification and knowledge discovery.
引用
收藏
页码:465 / 472
页数:8
相关论文
共 12 条
  • [1] [Anonymous], 1998, FEATURE EXTRACTION C
  • [2] [Anonymous], 1989, GENETIC ALGORITHM SE
  • [3] Dorigo M, 1999, NEW IDEAS OPTIMIZATI, P11
  • [4] DORIGO M, 1991, ANT SYSTEM AUTOCATAL
  • [5] Fogel D.B., 1995, EVOLUTIONARY COMPUTA
  • [6] Graph-based Ant System and its convergence
    Gutjahr, WJ
    [J]. FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE, 2000, 16 (08): : 873 - 888
  • [7] HOLLAND JH, 1992, ADAPTATION NATURAL A
  • [8] Keeney R.L., 1976, DECISIONS MULTIPLE O
  • [9] Koza J.R., 1992, GENETIC PROGRAMMING
  • [10] Mitchell M., 1996, INTRO GENETIC ALGORI