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.
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References
Butz, M.V., Wilson, S.W.: An algorithmic description of XCS. Soft Computing 6, 144–152 (2002)
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)
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)
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)
Holmes, J.H., Bilker, W.B.: The effect of missing data on learning classifier system classification and prediction performance
Lanzi, P.L., Stolzmann, W., Wilson, S.W. (eds.): Advances in Learning Classifier Systems. LNCS (LNAI), vol. 2661, pp. 46–60. Springer, Heidelberg (2003)
Lanzi, P.L.: Learning classifier systems from a reinforcement learning perspective. Soft Computing 6(3-4), 162–170 (2002)
Rulequest Systems, http://www.rulequest.com
Smith, S.: A learning system based on genetic algorithms. Ph.D. dissertation. University of Pittsburgh (1980)
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)
Wilson, S.W.: Classifier fitness based on accuracy. Evolutionary Computation 3(2), 149–175 (1995)
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© 2005 Springer-Verlag Berlin Heidelberg
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Holmes, J.H., Sager, J.A. (2005). Rule Discovery in Epidemiologic Surveillance Data Using EpiXCS: An Evolutionary Computation Approach. In: Miksch, S., Hunter, J., Keravnou, E.T. (eds) Artificial Intelligence in Medicine. AIME 2005. Lecture Notes in Computer Science(), vol 3581. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11527770_60
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DOI: https://doi.org/10.1007/11527770_60
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-27831-3
Online ISBN: 978-3-540-31884-2
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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.