Improving the Performance of a Pittsburgh Learning Classifier System Using a Default Rule

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


An interesting feature of encoding the individuals of a Pittsburgh learning classifier system as a decision list is the emergent generation of a default rule. However, performance of the system is strongly tied to the learning system choosing the correct class for this default rule. In this paper we experimentally study the use of an explicit (static) default rule. We first test simple policies for setting the class of the default rule, such as the majority/minority class of the problem. Next, we introduce some techniques to automatically determine the most suitable class.


Knowledge Representation Minority Class Default Rule Learn Classifier System Decision List 
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 Berlin Heidelberg 2007

Authors and Affiliations

  • Jaume Bacardit
    • 1
  • David E. Goldberg
    • 2
  • Martin V. Butz
    • 3
  1. 1.ASAP, School of Computer Science and IT, University of Nottingham, Jubilee Campus, Wollaton Road, Nottingham, NG8 1BBUK
  2. 2.Illinois Genetic Algorithms Laboratory (IlliGAL), Department of General Engineering, University of Illinois at Urbana-Champaign, 104 S. Mathews Ave, Urbana, IL 61801 
  3. 3.Department of Cognitive Psychology, University of Würzburg, 97070 WürzburgGermany

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