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What Should a Classifier System Learn?

(And How Should we Measure it?)
  • Tim Kovacs
Part of the Distinguished Dissertations book series (DISTDISS)

Abstract

In this Chapter 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 perfor- mance metrics in two situations, and in the process find evidence of XCS’s strong bias against overlapping rules.

Keywords

Boolean Function Performance Metrics Classifier System Truth Table Minimal Representation 
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 London 2004

Authors and Affiliations

  • Tim Kovacs

There are no affiliations available

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