Data Mining in Learning Classifier Systems: Comparing XCS with GAssist

  • Jaume Bacardit
  • Martin V. Butz
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4399)

Abstract

This paper compares performance of the Pittsburgh-style system GAssist with the Michigan-style system XCS on several datamining problems. Our analysis shows that both systems are suitable for datamining but have different advantages and disadvantages. The study does not only reveal important differences between the two systems but also suggests several structural properties of the underlying datasets.

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References

  1. 1.
    Bacardit, J., Garrell, J.M.: Analysis and improvements of the adaptive discretization intervals knowledge representation. In: Deb, K., et al. (eds.) GECCO 2004. LNCS, vol. 3103, pp. 726–738. Springer, Heidelberg (2004)Google Scholar
  2. 2.
    Bacardit, J.: Pittsburgh Genetics-Based Machine Learning in the Data Mining era: Representations, generalization, and run-time. PhD thesis, Ramon Llull University, Barcelona, Catalonia, Spain (2004)Google Scholar
  3. 3.
    Bacardit, J., Garrell, J.M.: Bloat control and generalization pressure using the minimum description length principle for a pittsburgh approach learning classifier system. In: Proceedings of the 6th International Workshop on Learning Classifier Systems, Springer, Heidelberg (in press, 2003)Google Scholar
  4. 4.
    Bacardit, J., Garrell, J.M.: Incremental learning for pittsburgh approach classifier systems. In: Proceedings of the “Segundo Congreso Españl de Metaheurísticas, Algoritmos Evolutivos y Bioinspirados”, pp. 303–311 (2003)Google Scholar
  5. 5.
    Bernadó, E., Llorà, X., Garrell, J.M.: XCS and GALE: a comparative study of two learning classifier systems with six other learning algorithms on classification tasks. In: Fourth International Workshop on Learning Classifier Systems - IWLCS-2001, pp. 337–341 (2001)Google Scholar
  6. 6.
    Blake, C., Keogh, E., Merz, C.: UCI repository of machine learning databases (1998), http://www.ics.uci.edu/mlearn/MLRepository.html
  7. 7.
    Butz, M.V.: Rule-Based Evolutionary Online Learning Systems: A Principled Approach to LCS Analysis and Design. Studies in Fuzziness and Soft Computing. Springer, Heidelberg (2005)Google Scholar
  8. 8.
    Butz, M.V., Sastry, K., Goldberg, D.E.: Tournament selection in XCS. In: Proceedings of the Fifth Genetic and Evolutionary Computation Conference (GECCO-2003), pp. 1857–1869 (2003)Google Scholar
  9. 9.
    DeJong, K.A., Spears, W.M., Gordon, D.F.: Using genetic algorithms for concept learning. Machine Learning 13(2/3), 161–188 (1993)CrossRefGoogle Scholar
  10. 10.
    Llorà, X., Garrell, J.M.: Knowledge-independent data mining with fine-grained parallel evolutionary algorithms. In: Proceedings of the Third Genetic and Evolutionary Computation Conference, pp. 461–468. Morgan Kaufmann, San Francisco (2001)Google Scholar
  11. 11.
    Wilson, S.W.: Classifier fitness based on accuracy. Evolutionary Computation 3(2), 149–175 (1995)CrossRefGoogle Scholar
  12. 12.
    Wilson, S.W.: Get real! XCS with continuous-valued inputs. In: Booker, L., et al. (eds.) Festschrift in Honor of John H. Holland, pp. 111–121. Center for the Study of Complex Systems (1999)Google Scholar

Copyright information

© Springer Berlin Heidelberg 2007

Authors and Affiliations

  • Jaume Bacardit
    • 1
  • Martin V. Butz
    • 2
  1. 1.ASAP, School of Computer Science and IT, University of Nottingham, Jubilee Campus, Wollaton Road, Nottingham, NG8 1BBUK
  2. 2.Department of Cognitive Psychology, University of Würzburg, 97070 WürzburgGermany

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