What should a classifier system learn and how should we measure it?

T Kovacs

Research output: Contribution to journalArticle (Academic Journal)peer-review

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. 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.
Translated title of the contributionWhat should a classifier system learn and how should we measure it?
Original languageEnglish
Pages (from-to)171 - 182
Number of pages11
JournalJournal of Soft Computing
Volume6 (3-4)
Publication statusPublished - Jun 2002

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