Experimental Comparison of Feature Subset Selection Using GA and ACO Algorithm

  • Keunjoon Lee
  • Jinu Joo
  • Jihoon Yang
  • Vasant Honavar
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4093)


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.


Candidate Solution Feature Subset Selection Elite Policy World Pattern Construct Neural Network 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. 1.
    Goldberg, D.: Genetic Algorithms in Search, Optimization, and Machine Learning. Addison-Wesley, New York (1989)MATHGoogle Scholar
  2. 2.
    Holland, J.: Adaptation in Natural and Artificial Systems. MIT Press, Cambridge (1992)Google Scholar
  3. 3.
    Koza, J.: Genetic Programming: On the Programming of Computers by Means of Natural Selection. MIT Press, Cambridge, MA (1992)MATHGoogle Scholar
  4. 4.
    Fogel, D.: Evolutionary Computation: Toward a New Philosophy of Machine Intelligence. IEEE Press, Piscataway (1995)Google Scholar
  5. 5.
    Dorigo, M., Maniezzo, V., Colorni, A.: The ant system: An autocatalytic optimizing process (1991)Google Scholar
  6. 6.
    Dorigo, M., Di Caro, G.: The ant colony optimization meta-heuristic. In: Corne, D., Dorigo, M., Glover, F. (eds.) New Ideas in Optimization, pp. 11–32. McGraw-Hill, London (1999)Google Scholar
  7. 7.
    Gutjahr, W.J.: A graph-based ant system and its convergence. Future Gener. Comput. Syst. 16(9), 873–888 (2000)CrossRefGoogle Scholar
  8. 8.
    Yang, J., Parekh, R., Honavar, V.: Distal: An inter-pattern distance-based constructive learning algorithm. In: Proceedings of the International Joint Conference on Neural Networks, Anchorage, Alaska, pp. 2208–2213 (1998)Google Scholar
  9. 9.
    Mitchell, M.: An Introduction to Genetic algorithms. MIT Press, Cambridge (1996)Google Scholar
  10. 10.
    Yang, J., Honavar, V.: Feature subset selection using a genetic algorithm. In: Motoda, Liu (eds.) Feature Extraction, Construction and Selection - A Data Mining Perspective, pp. 117–136. Kluwer Academic Publishers, Dordrecht (1998)Google Scholar
  11. 11.
    Murphy, P., Aha, D.: Uci repository of machine learning databases. Department of Information and Computer Science, University of California, Irvine, CA (1994)Google Scholar
  12. 12.
    Keeney, R., Raiffa, H.: Decisions with Multiple Objectives: Preferences and Value Tradeoffs. Wiley, New York (1976)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Keunjoon Lee
    • 1
  • Jinu Joo
    • 2
  • Jihoon Yang
    • 3
  • Vasant Honavar
    • 4
  1. 1.Kookmin Bank, Sejong Daewoo B/DJongno-KuKorea
  2. 2.Development Laboratory 1, Mobile Handset R&D CenterMobile Communications Company, LG Electronics Inc., Gasan-DongGumchon-KuKorea
  3. 3.Department of Computer ScienceSogang UniversityMapo-KuKorea
  4. 4.Artificial Intelligence Research Laboratory, Department of Computer ScienceIowa State University AmesUSA

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