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)


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.


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.


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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

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