Building Compact Rulesets for Describing Continuous-Valued Problem Spaces Using a Learning Classifier System

  • D. Wyatt
  • L. Bull
  • I. C. Parmee


Learning Classifier Systems have previously been shown to have some application in deducing the characteristics of complex multi-modal test environments to a suitable level of accuracy. In this study, the issue of presenting human-readable rulesets to a potential user is addressed. In particular, two existing ruleset compaction algorithms originally devised for rulesets with an integer-valued representation are applied to rulesets with a continuous-valued representation. The algorithms are used to reduce the size of rulesets evolved by the XCS classifier system. Following initial testing, both algorithms are modified to take into account problems associated with the new representation. Finally, the modified algorithms are shown to outperform the originals.


Classifier System Minority Class Learn Classifier System Class Imbalance Problem Wisconsin Breast Cancer 
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  1. 1.
    Beasley D, Bull D, Martin R (1993) A Sequential Niche Technique for Multimodal Function Optimisation. Evolutionary Computation, 1(2); 101–125CrossRefGoogle Scholar
  2. 2.
    Blake C, Merz C (1998) UCI Repository of Machine Learning DatabasesGoogle Scholar
  3. 3.
    Bull L, Wyatt D, Parmee I (2002) Initial Modifications to XCS for use in Interactive Evolutionary Design. In: Parallel Problem Solving From Nature-PPSN VII, Springer Verlag; 568–577Google Scholar
  4. 4.
    Butz M, Wilson S (2001) An Algorithmic Description of XCS. In: Advances in Learning Classifier Systems. Third International Workshop (IWLCS-2000), Lecture Notes in AI (LNAI-1996). Springer-Verlag:Berlin; 253–272Google Scholar
  5. 5.
    Fu C, Davis L (2002) A Modified Classifier System Compaction Algorithm, Proc. of the Genetic and Evolutionary Computation Conference 2002; 920–925Google Scholar
  6. 6.
    Holland J (1975) Adaptation in Natural and Artificial Systems, MIT PressGoogle Scholar
  7. 7.
    Holland J (1986) Escaping Brittleness: the Possibilities of General-Purpose Learning Algorithms Applied to Parallel Rule-based Systems. In: Machine Learning, An Artificial Intelligence Approach. Morgan KaufmannGoogle Scholar
  8. 8.
    Japkowicz N, Stephen S (2002) The Class Imbalance Problem: A Systematic Study. Intelligent Data Analysis, 6(5); 429–450MATHGoogle Scholar
  9. 9.
    Kaelbling L, Littman M, Moore A (1996) Reinforcement Learning: A Survey. Journal of Artificial Intelligence Research 4; 237–285Google Scholar
  10. 10.
    Kocis L, Whiten W (1997) Computational Investigations in Low Discrepancy Sequences, ACM Trans. on Mathematical Software, 23(2); 266–294CrossRefMATHGoogle Scholar
  11. 11.
    Parmee IC (1996) The Maintenance of Search Diversity for Effective Design Space Decomposition using Cluster-Oriented Genetic Algorithms (COGAs) and Multi-Agent Strategies (GAANT). In: Proc. of 2nd International Conf. on Adaptive Computing in Engineering Design and Control; 128–138Google Scholar
  12. 12.
    Parmee IC (2002) Improving Problem Definition through Interactive Evolutionary Computation. In: Journal of Artificial Intelligence in Engineering Design, Analysis and Manufacture, 16(3)Google Scholar
  13. 13.
    Stone C, Bull L (2003) For Real! XCS with Continuous-Valued Inputs. Evolutionary Computation 11(3); 299–336CrossRefGoogle Scholar
  14. 14.
    Wilson S (1995) Classifier Fitness Based on Accuracy. Evolutionary Computation 3(2); 149–175CrossRefGoogle Scholar
  15. 15.
    Wilson S (2000) Get real! XCS with Continuous-valued inputs. In: Learning Classifier Systems. From Foundations to Applications Lecture Notes in Artificial Intelligence (LNAI-1813) Springer-Verlag: Berlin; 209–222Google Scholar
  16. 16.
    Wilson S (2001) Compact Rulesets for XCSI. In: Advances in Learning Classifier Systems. Fourth International Workshop (IWLCS-2001), Lecture Notes in Artificial Intelligence (LNAI-2321). Springer-Verlag:Berlin; 197–210Google Scholar
  17. 17.
    Wilson S (2001) Mining Oblique Data with XCS, In: Advances in Learning Classifier Systems. Third International Workshop (IWLCS-2000), Lecture Notes in Artificial Intelligence (LNAI-1996). Springer-Verlag:Berlin; 158–177Google Scholar
  18. 18.
    Wyatt D, Bull L (2003) Using XCS to Describe Continuous-Valued Problem Spaces. Technical Report UWELCSG03-004Google Scholar

Copyright information

© Springer-Verlag London 2004

Authors and Affiliations

  • D. Wyatt
    • 1
  • L. Bull
    • 1
  • I. C. Parmee
    • 1
  1. 1.Faculty of Computing, Engineering and Mathematical SciencesUniversity of the West of EnglandBristol

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