Analysis and improvements of the classifier error estimate in XCSF

Abstract

The estimation of the classifier error plays a key role in accuracy-based learning classifier systems. In this paper we study the current definition of the classifier error in XCSF and discuss the limitations of the algorithm that is currently used to compute the classifier error estimate from online experience. Subsequently, we introduce a new definition for the classifier error and apply the Bayes Linear Analysis framework to find a more accurate and reliable error estimate. This results in two incremental error estimate update algorithms that we compare empirically to the performance of the currently applied approach. Our results suggest that the new estimation algorithms can improve the generalization capabilities of XCSF, especially when the action-set subsumption operator is used. © 2008 Springer Berlin Heidelberg.