Feature Selection Using Ant Colony Optimization (ACO): A New Method and Comparative Study in the Application of Face Recognition System

  • Hamidreza Rashidy Kanan
  • Karim Faez
  • Sayyed Mostafa Taheri
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4597)


Feature Selection (FS) and reduction of pattern dimensionality is a most important step in pattern recognition systems. One approach in the feature selection area is employing population-based optimization algorithms such as Genetic Algorithm (GA)-based method and Ant Colony Optimization (ACO)-based method. This paper presents a novel feature selection method that is based on Ant Colony Optimization (ACO). ACO algorithm is inspired of ant’s social behavior in their search for the shortest paths to food sources. Most common techniques for ACO-Based feature selection use the priori information of features. However, in the proposed algorithm, classifier performance and the length of selected feature vector are adopted as heuristic information for ACO. So, we can select the optimal feature subset without the priori information of features. This approach is easily implemented and because of using one simple classifier in it, its computational complexity is very low. Simulation results on face recognition system and ORL database show the superiority of the proposed algorithm.


Feature Selection Ant Colony Optimization (ACO) Genetic Algorithm Face Recognition 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    kml, L., Kittler, J.: Feature set search algorithms. In: Chen, C.H. (ed.) Pattern Recognition and Signal Processing, Sijhoff and Noordhoff, the Netherlands (1978)Google Scholar
  2. 2.
    Ani, A.A.: An Ant Colony Optimization Based Approach for Feature Selection. In: Proceeding of AIML Conference (2005)Google Scholar
  3. 3.
    Jensen, R.: Combining rough and fuzzy sets for feature selection. Ph.D. Thesis, University of Edinburgh (2005)Google Scholar
  4. 4.
    Kohavi, R.: Feature Subset Selection as search with Probabilistic Estimates. AAAI Fall Symposium On Relevance (1994)Google Scholar
  5. 5.
    Srinivas, M., Patnik, L.M.: Genetic Algorithms: A Survey. IEEE Computer Society Press, Los Alamitos (1994)Google Scholar
  6. 6.
    Dorigo, M., Caro, G.D.: Ant Colony Optimization: A New Meta-heuristic. In: Proceeding of the Congress on Evolutionary Computing (1999)Google Scholar
  7. 7.
    Bonabeau, E., Dorigo, M., Theraulaz, G.: Swarm Intelligence: From Natural to Artificial Systems. Oxford University Press, New York (1999)zbMATHGoogle Scholar
  8. 8.
    Liu, B., Abbass, H.A., McKay, B.: Classification Rule Discovery with Ant Colony Optimization. IEEE Computational Intelligence 3(1) (2004)Google Scholar
  9. 9.
    Dorigo, M., Maniezzo, V., Colorni, A.: The Ant System: Optimization by a Colony of Cooperating Agents. IEEE Transactions on Systems, Man, and Cybernetics, Part B 26(1), 29–41 (1996)CrossRefGoogle Scholar
  10. 10.
    Maniezzo, V., Colorni, A.: The Ant System Applied to the Quadratic Assignment Problem. Knowledge and Data Engineering 11(5), 769–778 (1999)CrossRefGoogle Scholar
  11. 11.
    Duda, R.O., Hart, P.E.: Pattern Recognition and Scene Analysis. Wiley, Chichester (1973)Google Scholar
  12. 12.
    Pudil, P., Novovicova, J., Kittler, J.: Floating search methods in feature selection. Pattern Recognition Letters 15, 1119–1125 (1994)CrossRefGoogle Scholar
  13. 13.
    Siedlecki, W., Sklansky, J.: A note on genetic algorithms for large-scale feature selection. Pattern Recognition Letters 10(5), 335–347 (1989)zbMATHCrossRefGoogle Scholar
  14. 14.
    Ani, A.A.: Ant Colony Optimization for Feature Subset Selection. Transactions On Engineering, Computing And Technology 4 (2005)Google Scholar
  15. 15.
    Zhang, C.K., Hu, H.: Feature Selection Using The Hybrid Of Ant Colony Optimization and Mutual Information For The Forecaster. In: Proceedings of the Fourth International Conference on Machine Learning and Cybernetics (2005)Google Scholar
  16. 16.
    Gao, H.H., Yang, H.H., Wang, X.Y.: Ant Colony Optimization Based Network Intrusion Feature Selection And Detection. In: Proceedings of the Fourth International Conference on Machine Learning and Cybernetics (2005)Google Scholar
  17. 17.
    Bins, J.: Feature Selection of Huge Feature Sets in the Context of Computer Vision. Ph.D. Dissertation, Computer Science Department, Colorado State University (2000)Google Scholar
  18. 18.
    Siedlecki, W., Sklansky, J.: On Automatic Feature Selection. International Journal of Pattern Recognition and Artificial Intelligence 2(2), 197–220 (1988)CrossRefGoogle Scholar
  19. 19.
    Dorigo, M., Blum, C.: Ant colony optimization theory: A survey. Theoretical Computer Science 344, 243–278 (2005)zbMATHCrossRefGoogle Scholar
  20. 20.
    Pawlak, Z.: Rough Sets: Theoretical Aspects of Reasoning About Data. Kluwer Academic Publishing, Dordrecht (1991)zbMATHGoogle Scholar
  21. 21.
    Yang, J., Honavar, V.: Feature Subset Selection Using a Genetic Algorithm. IEEE Intelligent Systems 13, 44–49 (1998)CrossRefGoogle Scholar
  22. 22.
    Punch, W.F., Goodman, E.D., Pei, L.C.S.M., Hovland, P., Enbody, R.: Further research on Feature Selection and Classification using Genetic Algorithms. In: Proc. Int. Conf. Genetic Algorithms, pp. 557–564 (1993)Google Scholar
  23. 23.
    Raymer, M., Punch, W., Goodman, E., Kuhn, L., Jain, A.K.: Dimensionality Reduction Using Genetic Algorithms. IEEE Transactions on Evolutionary Computing 4, 164–171 (2000)CrossRefGoogle Scholar
  24. 24.
    Rashidy Kanan, H., Faez, K., Ezoji, M.: Face Recognition: An Optimized Localization Approach and Selected PZMI Feature Vector Using SVM Classifier. In: Huang, D.-S., Li, K., Irwin, G.W. (eds.) ICIC 2006. LNCS, vol. 4113, pp. 690–696. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  25. 25.
    Rashidy Kanan, H., Faez, K., Ezoji, M.: An Efficient Face Recognition System Using a New Optimized Localization Method. In: ICPR 2006. Proceeding of the 18th International Conference on Pattern Recognition (2006)Google Scholar
  26. 26.
    Rashidy Kanan, H., Faez, K.: ZMI and Wavelet Transform Features and SVM Classifier in the Optimized Face Recognition system. In: ISSPIT 2005. Proceeding of the 5th IEEE International Symposium on Signal Processing and Information Technology, pp. 295–300. IEEE Computer Society Press, Los Alamitos (2005)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Hamidreza Rashidy Kanan
    • 1
  • Karim Faez
    • 1
  • Sayyed Mostafa Taheri
    • 1
  1. 1.Image Processing and Pattern Recognition Lab, Electrical Engineering Department, Amirkabir University of Technology (Tehran Polytechnic), Hafez Avenue, Tehran, 15914Iran

Personalised recommendations