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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© 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
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