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What should a classifier system learn?

Tim Kovacs, What should a classifier system learn?. Proceedings of the 2001 Congress on Evolutionary Computation (CEC). Jong-Hwan Kim, (eds.), pp. 775–782. May 2001. PDF, 113 Kbytes.

Abstract

We consider the issues of how a classifier system should learn to represent a Boolean function, and how we should measure its progress in doing so. We identify four properties which may be desirable of a representation; that it be complete, accurate, minimal and non-overlapping, and distinguish variations on two of these properties for the XCS system. We distinguish two categories of learning metric, introduce new metrics and evaluate them. We demonstrate the superiority of population state metrics over performance metrics in two situations, and in the process find evidence of XCS's strong bias against overlapping rules.

[For a revised version please see the chapter in "Strength or Accuracy: Credit Assignment in Learning Classifier Systems" PhD Thesis, 2002. School of Computer Science. University of Birmingham. Birmingham, U.K. http://www.cs.bris.ac.uk/~kovacs/author.directory/thesis/thesis.html]

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