Analysis and Improvements of the Classifier Error Estimate in XCSF

Authors:
Daniele Loiacono;Jan Drugowitsch;Alwyn Barry;Pier Luca Lanzi
Affiliations:
Artificial Intelligence and Robotics Laboratory (AIRLab), Politecnico di Milano, Milano, Italy I-20133;Department of Computer Science, University of Bath, UK;Department of Computer Science, University of Bath, UK;Artificial Intelligence and Robotics Laboratory (AIRLab), Politecnico di Milano, Milano, Italy I-20133 and Illinois Genetic Algorithm Laboratory (IlliGAL), University of Illinois at Urbana Champai ...
Venue:
Learning Classifier Systems
Year:
2008

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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.