Speeding-Up Pittsburgh Learning Classifier Systems: Modeling Time and Accuracy

  • Jaume Bacardit
  • David E. Goldberg
  • Martin V. Butz
  • Xavier Llorà
  • Josep M. Garrell
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3242)


Windowing methods are useful techniques to reduce the computational cost of Pittsburgh-style genetic-based machine learning techniques. If used properly, they additionally can be used to improve the classification accuracy of the system. In this paper we develop a theoretical framework for a windowing scheme called ILAS, developed previously by the authors. The framework allows us to approximate the degree of windowing we can apply to a given dataset as well as the gain in run-time. The framework sets the first stage for the development of a larger methodology with several types of learning strategies in which we can apply ILAS, such as maximizing the learning performance of the system, or achieving the maximum run-time reduction without significant accuracy loss.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Holland, J.H.: Adaptation in Natural and Artificial Systems. University of Michigan Press (1975)Google Scholar
  2. 2.
    Goldberg, D.E.: Genetic Algorithms in Search, Optimization and Machine Learning. Addison-Wesley Publishing Company, Inc., Reading (1989)zbMATHGoogle Scholar
  3. 3.
    Smith, S.F.: Flexible learning of problem solving heuristics through adaptive search. In: Proceedings of the Eighth International Joint Conference on Artificial Intelligence, Los Altos, CA, pp. 421–425. Morgan Kaufmann, San Francisco (1983)Google Scholar
  4. 4.
    Soule, T., Foster, J.A.: Effects of code growth and parsimony pressure on populations in genetic programming. Evolutionary Computation 6, 293–309 (1998)CrossRefGoogle Scholar
  5. 5.
    Bacardit, J., Garrell, J.M.: Incremental learning for pittsburgh approach classifier systems. In: Proceedings of the “Segundo Congreso Español de Metaheurísticas, Algoritmos Evolutivos y Bioinspirados, pp. 303–311 (2003)Google Scholar
  6. 6.
    Bacardit, J., Garrell, J.M.: Comparison of training set reduction techniques for pittsburgh approach genetic classifier systems. In: Proceedings of the “X Conferencia de la Asociación Española para la Inteligencia Artificial, CAEPIA 2003 (2003)Google Scholar
  7. 7.
    DeJong, K.A., Spears, W.M., Gordon, D.F.: Using genetic algorithms for concept learning. Machine Learning 13, 161–188 (1993)CrossRefGoogle Scholar
  8. 8.
    Fürnkranz, J.: Integrative windowing. Journal of Artificial Intelligence Research 8, 129–164 (1998)zbMATHGoogle Scholar
  9. 9.
    Salamó, M., Golobardes, E.: Hybrid deletion policies for case base maintenance. In: Proceedings of FLAIRS 2003, pp. 150–154 (2003)Google Scholar
  10. 10.
    Bacardit, J., Garrell, J.M.: Evolving multiple discretizations with adaptive intervals for a pittsburgh rule-based learning classifier system. In: Cantú-Paz, E., Foster, J.A., Deb, K., Davis, L., Roy, R., O’Reilly, U.-M., Beyer, H.-G., Kendall, G., Wilson, S.W., Harman, M., Wegener, J., Dasgupta, D., Potter, M.A., Schultz, A., Dowsland, K.A., Jonoska, N., Miller, J., Standish, R.K. (eds.) GECCO 2003. LNCS, vol. 2724, pp. 1818–1831. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  11. 11.
    Rivest, R.L.: Learning decision lists. Machine Learning 2, 229–246 (1987)MathSciNetGoogle Scholar
  12. 12.
    Bacardit, J., Garrell, J.M.: Bloat control and generalization pressure using the minimum description length principle for a pittsburgh approach learning classifier system. In: Kovacs, T., Llorà, X., Takadama, K., Lanzi, P.L., Stolzmann, W., Wilson, S.W. (eds.) IWLCS 2003. LNCS (LNAI), vol. 4399, pp. 59–79. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  13. 13.
    Blake, C., Keogh, E., Merz, C.: UCI repository of machine learning databases (1998),
  14. 14.
    Wilson, S.W.: Classifier fitness based on accuracy. Evolutionary Computation 3, 149–175 (1995)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • Jaume Bacardit
    • 1
  • David E. Goldberg
    • 2
  • Martin V. Butz
    • 2
  • Xavier Llorà
    • 2
  • Josep M. Garrell
    • 1
  1. 1.Intelligent Systems Research GroupUniversitat Ramon LlullBarcelona, CataloniaSpain
  2. 2.Illinois Genetic Algorithms Laboratory (IlliGAL), Department of General EngineeringUniversity of Illinois at Urbana-ChampaignUrbanaUSA

Personalised recommendations