Advertisement

Rule Discovery in Epidemiologic Surveillance Data Using EpiXCS: An Evolutionary Computation Approach

  • John H. Holmes
  • Jennifer A. Sager
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3581)

Abstract

This paper describes the architecture and application of EpiXCS, a learning classifier system that uses reinforcement learning and the genetic algorithm to discover rule-based knowledge in epidemiologic surveillance databases. EpiXCS implements several additional features that tailor the XCS paradigm to the demands of epidemiologic data and users who are not familiar with learning classifier systems. These include a workbench-style interface for visualization and parameterization and the use of clinically meaningful evaluation metrics. EpiXCS has been applied to a large surveillance database, and shown to discover classification rules similarly to See5, a well-known decision tree inducer.

Keywords

Classifier Fitness Rule Discovery Learn Classifier System Classifier Population Fatality Analysis Reporting System 
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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Butz, M.V., Wilson, S.W.: An algorithmic description of XCS. Soft Computing 6, 144–152 (2002)zbMATHGoogle Scholar
  2. 2.
    Butz, M.V., Kovacs, T., Lanzi, P.L., Wilson, S.W.: Toward a theory of generalization and learning in XCS. IEEE Transactions on Evolutionary Computation 8(1), 28–46 (2004)CrossRefGoogle Scholar
  3. 3.
    Holland, J.H., Reitman, J.: Cognitive systems based in adaptive algorithms. In: Waterman, D., Hayes-Roth, F. (eds.) Pattern-directed inference systems. Academic Press, New York (1978)Google Scholar
  4. 4.
    Holmes, J.H., Lanzi, P.L., Stolzmann, W., Wilson, S.W.: Learning classifier systems: new models, successful applications. Information Processing Letters 82(1), 23–30 (2002)zbMATHCrossRefMathSciNetGoogle Scholar
  5. 5.
    Holmes, J.H., Bilker, W.B.: The effect of missing data on learning classifier system classification and prediction performanceGoogle Scholar
  6. 6.
    Lanzi, P.L., Stolzmann, W., Wilson, S.W. (eds.): Advances in Learning Classifier Systems. LNCS (LNAI), vol. 2661, pp. 46–60. Springer, Heidelberg (2003)Google Scholar
  7. 7.
    Lanzi, P.L.: Learning classifier systems from a reinforcement learning perspective. Soft Computing 6(3-4), 162–170 (2002)zbMATHGoogle Scholar
  8. 8.
    Rulequest Systems, http://www.rulequest.com
  9. 9.
    Smith, S.: A learning system based on genetic algorithms. Ph.D. dissertation. University of Pittsburgh (1980)Google Scholar
  10. 10.
    Wilson, S.W.: Knowledge growth in an artificial animal. In: Grefenstette, JJ. Proceedings of the First International Conference on Genetic Algorithms, pp. 16–23. Lawrence Erlbaum Associates, Mahwah (1985)Google Scholar
  11. 11.
    Wilson, S.W.: Classifier fitness based on accuracy. Evolutionary Computation 3(2), 149–175 (1995)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • John H. Holmes
    • 1
  • Jennifer A. Sager
    • 2
  1. 1.University of Pennsylvania School of MedicinePhiladelphiaUSA
  2. 2.University of New Mexico Department of Computer ScienceAlbuquerqueUSA

Personalised recommendations