Rule Discovery in Epidemiologic Surveillance Data Using EpiXCS: An Evolutionary Computation Approach
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.
KeywordsClassifier Fitness Rule Discovery Learn Classifier System Classifier Population Fatality Analysis Reporting System
Unable to display preview. Download preview PDF.
- 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
- 5.Holmes, J.H., Bilker, W.B.: The effect of missing data on learning classifier system classification and prediction performanceGoogle Scholar
- 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
- 8.Rulequest Systems, http://www.rulequest.com
- 9.Smith, S.: A learning system based on genetic algorithms. Ph.D. dissertation. University of Pittsburgh (1980)Google Scholar
- 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