Data Mining in Learning Classifier Systems: Comparing XCS with GAssist

  • Jaume Bacardit
  • Martin V. Butz
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4399)


This paper compares performance of the Pittsburgh-style system GAssist with the Michigan-style system XCS on several datamining problems. Our analysis shows that both systems are suitable for datamining but have different advantages and disadvantages. The study does not only reveal important differences between the two systems but also suggests several structural properties of the underlying datasets.


Conjunctive Normal Form Minority Class Incremental Learning Minimum Description Length Tournament Selection 
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Copyright information

© Springer Berlin Heidelberg 2007

Authors and Affiliations

  • Jaume Bacardit
    • 1
  • Martin V. Butz
    • 2
  1. 1.ASAP, School of Computer Science and IT, University of Nottingham, Jubilee Campus, Wollaton Road, Nottingham, NG8 1BBUK
  2. 2.Department of Cognitive Psychology, University of Würzburg, 97070 WürzburgGermany

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