DOI QR코드

DOI QR Code

Convergence Characteristics of Ant Colony Optimization with Selective Evaluation in Feature Selection

특징 선택에서 선택적 평가를 사용하는 개미 군집 최적화의 수렴 특성

  • 이진선 (우석대학교 게임콘텐츠학과) ;
  • 오일석 (전북대학교 컴퓨터공학부/영상정보신기술연구소)
  • Received : 2011.09.07
  • Accepted : 2011.09.23
  • Published : 2011.10.28

Abstract

In feature selection, the selective evaluation scheme for Ant Colony Optimization(ACO) has recently been proposed, which reduces computational load by excluding unnecessary or less promising candidate solutions from the actual evaluation. Its superiority was supported by experimental results. However the experiment seems to be not statistically sufficient since it used only one dataset. The aim of this paper is to analyze convergence characteristics of the selective evaluation scheme and to make the conclusion more convincing. We chose three datasets related to handwriting, medical, and speech domains from UCI repository whose feature set size ranges from 256 to 617. For each of them, we executed 12 independent runs in order to obtain statistically stable data. Each run was given 72 hours to observe the long-time convergence. Based on analysis of experimental data, we describe a reason for the superiority and where the scheme can be applied.

Keywords

Pattern Recognition;Feature Selection;Convergence;Meta-heuristics;Selective Evaluation

References

  1. J. Kittler, "Feature selection and extraction," in Handbook of Pattern Recognition and Image Processing, Academic Press (Edited by T.Y.Young and K.S.Fu), pp.59-83, 1986.
  2. M. H. Aghdam, N. Ghasem-Aghaee, and M. E. Basiri, "Text feature selection using ant colony optimization," Expert Systems with Applications, Vol.36, pp.6843-6853, 2009. https://doi.org/10.1016/j.eswa.2008.08.022
  3. A. Al-Ani, "Feature subset selection using ant colony optimization," International Journal of Computational Intelligence, Vol.2, No.1, pp.53-58, 2006.
  4. M. E. Basiri and S. Nemati, "A novel hybrid ACO-GA algorithm for text feature selection," IEEE Congresson Evolutionary Computation, pp.2561-2568, 2009. https://doi.org/10.1109/CEC.2009.4983263
  5. K. J. Lee, J. Joo, J. Yang, and V. Honavar, "Experimental comparison of feature subset selection using GA and ACO algorithm," Lecture Notes in Computer Science (Advanced Data Mining and Applications), Vol.4093, pp.465-472, 2006.
  6. 오일석, 이진선, "패턴 인식에서 특징 선택을 위한 개미 군락 최적화," 한국콘텐츠학회논문지, 제10권, 제5호, pp.1-9, 2010. https://doi.org/10.5392/JKCA.2010.10.5.001
  7. S. M. Vieira, J. M. C. Sousa, and T. A. Runkler, "Fuzzy classification in ant feature selection," IEEE International Conference on Fuzzy Systems, pp.1763-1769, 2008. https://doi.org/10.1109/FUZZY.2008.4630609
  8. M. Dorigo, M. Birattari, and T. Stutzle, "Antcolony optimization," IEEE Computational Intelligence Magazine, Vol.1, No.4, pp.28-39, 2006. https://doi.org/10.1109/CI-M.2006.248054
  9. C. Solnon and D. Bridge, "An ant colony optimization meta-heuristic for subset selection problems," in System Engineering using Particle Swarm Optimization (Edited by Nadia Nedjah and Luiza Mourelle), Nova Science publisher, pp.7-29, 2006.
  10. I. S. Oh and J. S. Lee, "Ant colony optimization with null heuristic factor for feature selection," Proceedings of IEEE TENCON, 2009. https://doi.org/10.1109/TENCON.2009.5395862
  11. A. Asuncion and D. J. Newman, UCI Machine Learning Repository, Irvine, CA: University of California, School of Information and Computer Science, 2007.
  12. 오일석, 패턴인식, 교보문고, 2008.