A Ruleset Reduction Algorithm for the XCS Learning Classifier System

Abstract

XCS is a learning classifier system based on the original work by Stewart Wilson in 1995. It is has recently been found competitive with other state of the art machine learning techniques on benchmark data mining problems. For more general utility in this vein, however, issues are associated with the large numbers of classifiers produced by XCS; these issues concern both readability of the combined set of rules produced, and the overall processing time. The aim of this work is twofold, to produce reduced classifier sets which can more readily be understandable as rules, and to speedup processing via reduction of classifier set size during operation of XCS. A number of algorithmic modifications are presented, both in the operation of XCS itself and in the post-processing of the final set of classifiers. We describe a technique of qualifying classifiers for inclusion in action sets, which enables classifier sets to be generated prior to passing to a reduction algorithm, allowing reliable reductions to be performed with no performance penalty. The concepts of ‘spoilers’ and ‘uncertainty’ are introduced, which help to characterise some of the peculiarities of XCS in terms of operation and performance. A new reduction algorithm is described which we show to be similarly effective to Wilson’s recent technique, but with considerably more favourable time complexity, and we therefore suggest that it may be preferable to Wilson’s algorithm in many cases with particular requirements concerning the speed/performance tradeoff.