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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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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