The Journal of the Korea Contents Association (한국콘텐츠학회논문지)

The Korea Contents Association (한국콘텐츠학회)
권호 표지
DOI QR코드

DOI QR Code

Review on Genetic Algorithms for Pattern Recognition

패턴 인식을 위한 유전 알고리즘의 개관

  • 오일석 (전북대학교 전자정보공학부)
  • Published : 2007.01.28
Download PDF
⟨ Previous Next ⟩

Abstract

In pattern recognition field, there are many optimization problems having exponential search spaces. To solve of sequential search algorithms seeking sub-optimal solutions have been used. The algorithms have limitations of stopping at local optimums. Recently lots of researches attempt to solve the problems using genetic algorithms. This paper explains the huge search spaces of typical problems such as feature selection, classifier ensemble selection, neural network pruning, and clustering, and it reviews the genetic algorithms for solving them. Additionally we present several subjects worthy of noting as future researches.

패턴 인식 분야에는 지수적 탐색 공간을 가진 최적화 문제가 많이 있다. 이를 해결하기 위해 부 최적해를 구하는 순차 탐색 알고리즘이 사용되어 왔고, 이들 알고리즘은 국부 최적점에 빠지는 문제점을 안고 있다. 최근 이를 극복하기 위해 유전 알고리즘을 사용하는 사례가 많아졌다. 이 논문은 특징 선택, 분류기 앙상블 선택, 신경망 가지치기, 군집화 문제의 지수적 탐색 공간 특성을 설명하고 이를 해결하기 위한 유전 알고리즘을 살펴본다. 또한 향후 연구로서 가치가 높은 주제들에 대해 소개한다.

Keywords

References

  1. J. Holland, Adaptation in Nature and Artificial Systems, MIT Press, 1992.
  2. J. Kittler, ''Feature selection and extraction," in Handbook of Pattern Recognition and Irnage Processing, Academic Press (Edited by T.Y. Young and KS. Fu), pp.59-83, 1986.
  3. F. ]. Ferri, P. Pudil, M Hatef, and ]. Kittler, "Comparative study of techniques for large-s떠1e feature selection," in Pat따n Recognition in Practice IV (Ed. E.S. Gelsema and L.N. Kanal), Elsevier Science, pp.403-413, 1994.
  4. A. Jain and D. Zongker, ''Feature selection: evaluation, application, and small sample performance" IEEE Tr. PAMI, Vol.19, No.2, pp.153-158, Feb. 1997. https://doi.org/10.1109/34.574797
  5. M. Kudo and J. Sklansky，"Comparison of algorithms that select features for pattern recognition," Pattern Recontion, Vol.33, No.1, pp.25-41, 2000. https://doi.org/10.1016/S0031-3203(99)00041-2
  6. W. Sidlecki and J. Sklansky, "A note on genetic algorithms for large-scale feature selection," Pattern Recognition Letters, Vol.10 pp335-347, 1989 https://doi.org/10.1016/0167-8655(89)90037-8
  7. F. Z. Brill, D. E. Brown, and W. N. Martin ''Fastgenetic selection of features for neural network classifiers," IEE'E Tr. Neural Networks, Vol.3, No.2, pp.324-328, March 1992. https://doi.org/10.1109/72.125874
  8. J. H Yang and V. Honavar, ''Feature subset selection using a genetic algorithrn," IEEE intelligent Systems, Vol.l3, No2, pp.44-49, 1998. https://doi.org/10.1109/5254.671091
  9. L. I. Kuncheva and L. C. Jain，''Nearest neighbor classifier: simultaneous ending and feature selection," Pattern Recognition Letters, Vol.20, pp.1149-1156, 1999. https://doi.org/10.1016/S0167-8655(99)00082-3
  10. M. L. Raymer, W. F. Punch, E. D. Goodman，L. A. Kuhn. and A. K. Jain, "Dimensionality reduction using genetic algorithms," IEEE Tr. Evolutionary computation Vol.4, No.2, pp.164-171, July 2000 https://doi.org/10.1109/4235.850656
  11. J. S. Oh, J. S. Lee, and B. R. Moon, "Hybrid genetic algorithms for feature selection," IEEE Tr. on PAMI, Vol.26, No.11, pp.1424-1437， Nov. 2004, https://doi.org/10.1109/TPAMI.2004.105
  12. H. Bubke and A. Kandel, Hybrid Methods in Pattern Recognition, chapter 7-8, World Scientfic， 2002.
  13. Z. H. Zhou, J. X. Wu, and W. Tang, "Ensembling neural networks: Many could be better than all," Artificial Intelligence, Vol.137, pp.239-263, 2002 https://doi.org/10.1016/S0004-3702(02)00190-X
  14. L. S. Oliveira, R. Sabourin，F. Bortolozzi, and C. Y. Suen, ''Feature selection for ensembles: a hierarchical multi-objective genetic algorithm approach," ICDAR 2003.
  15. 김영원， 오일석， "유전 알고리즘에 의한 분류기 앙상블 선택" 한국정보과학회 컴퓨터비전 및 패턴인식 연구회 2005 추계 워크숍， 2005.
  16. R. Reed, "Pruning algorithms - a survey, " IEEE Tr. on Neural Networks, Vol.4, No.5, pp.740-747, Sep. 1993, https://doi.org/10.1109/72.248452
  17. D. Whitley and C. Bogart, "Thee evolution of connectivity: pruning neural networks using genetic algorithms，" International Joint Conf. on Neural Networks, Vol.1, Washinto DC, p.134, 1990.
  18. N. Dodd, "Optimization of network structure using genetic techniques" International Joint Conf. on Neural Networks, Vol.III, Washingto DC, pp.965-970, 1990.
  19. W. Wang, W. Lu, A Y. T. Leung, S. M. Lo, Z. Xu, and X. Wang, "Optimal feed-forward neural networks based on the combination of constructing and pruning by genetic algorithms，" Intemational Joint Conf. on Neural Networks, pp.636-641, 2002.
  20. G. Bebis, M Georgiopoulos, and T. Kasparis, "Coupling weight elimination and genetic algorithms，" IEEE International Conf. on Neural Networks, Vol.2, pp.1115-1120, 1996
  21. C. A Murthy and N. Chowdhury， "In search of optimal clusters using genetic algorithms," Pattern Recognition Letters, Vol.17, pp.825-832, 1996. https://doi.org/10.1016/0167-8655(96)00043-8
  22. P. Franti, J. Kivijarvi,T. Kaukoranta， and O. Nevalainen, "Genetic algorithms for large-scale clustering problems," The Computer Journal, Vol.40, No.9, pp.547-554，1997. https://doi.org/10.1093/comjnl/40.9.547
  23. L. Y. Tseng and S. B. Yang, "A genetic approach to the automatic clustering problem," Pattern Recongnition，Vol.34, pp.415-424, 2001. https://doi.org/10.1016/S0031-3203(00)00005-4
  24. S. Bandyopadhyay and U. Maulik，"Genetic clustering for automatic evolution of clusters and application to image classification," Pattern Recongtion， Vol.35, pp.1197 -1208, 2002. https://doi.org/10.1016/S0031-3203(01)00108-X
  25. P. Pudil, J. Novovicova, and J. Kittler, 'Floating search methods in feature selection," Pattern Recognition Letters, Vol.15, pp.1119-1125, 1994 https://doi.org/10.1016/0167-8655(94)90127-9
  26. A Endre Eiben, R. Hinterding, and Z. Michalewicz，"Parameter control in evolutionary algorithms," IEEE Tr. on Evolutionary Computation, Vol.3, No.2, pp.124-141, 1999. https://doi.org/10.1109/4235.771166
  27. E. Hart and P. Ross, "GAVEL - a new tool for genetic algorithm visualization，" IEEE Tr. on Evolutionary Computation, Vol.5, No.4, pp.335-348, 2001. https://doi.org/10.1109/4235.942528