Analysis and Improvements of the Classifier Error Estimate in XCSF

  • Daniele Loiacono
  • Jan Drugowitsch
  • Alwyn Barry
  • Pier Luca Lanzi
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4998)


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.


Root Mean Square Error Prediction Error Weight Vector Recursive Little Square Average Root Mean Square Error 
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 Berlin Heidelberg 2008

Authors and Affiliations

  • Daniele Loiacono
    • 1
  • Jan Drugowitsch
    • 2
  • Alwyn Barry
    • 2
  • Pier Luca Lanzi
    • 1
    • 3
  1. 1.Artificial Intelligence and Robotics Laboratory (AIRLab)Politecnico di MilanoMilanoItaly
  2. 2.Department of Computer ScienceUniversity of BathUK
  3. 3.Illinois Genetic Algorithm Laboratory (IlliGAL)University of Illinois at Urbana ChampaignUrbanaUSA

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