Improving the performance of a pittsburgh learning classifier system using a default rule

Authors:
Jaume Bacardit;David E. Goldberg;Martin V. Butz
Affiliations:
ASAP, School of Computer Science and IT, University of Nottingham, Nottingham, UK;Illinois Genetic Algorithms Laboratory, Department of General Engineering, University of Illinois at Urbana-Champaign, Urbana, IL;Department of Cognitive Psychology, University of Würzburg, Würzburg, Germany
Venue:
IWLCS'03-05 Proceedings of the 2003-2005 international conference on Learning classifier systems
Year:
2007

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Abstract

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.