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.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Drugowitsch, J., Barry, A.: A formal framework and extensions for function approximation in learning classifier systems. Machine Learning 70(1), 45–88 (2008)
Glantz, S.A., Slinker, B.K.: Primer of Applied Regression & Analysis of Variance, 2nd edn. McGraw Hill, New York (2001)
Goldstein, M.: Bayes linear analysis. In: Kotz, S., Read, C.B., Banks, D.L. (eds.) Encyclopedia of Statistical Sciences, vol. 3, pp. 29–34. Wiley, New York (1999)
Lanzi, P.L., Loiacono, D., Wilson, S.W., Goldberg, D.E.: Extending XCSF beyond linear approximation. In: Genetic and Evolutionary Computation – GECCO-2005. ACM Press, Washington (2005)
Lanzi, P.L., Loiacono, D., Wilson, S.W., Goldberg, D.E.: XCS with Computed Prediction for the Learning of Boolean Functions. In: Proceedings of the IEEE Congress on Evolutionary Computation – CEC-2005, Edinburgh, UK. IEEE Computer Society Press, Los Alamitos (2005)
Lanzi, P.L., Loiacono, D., Wilson, S.W., Goldberg, D.E.: Prediction update algorithms for XCSF: RLS, kalman filter, and gain adaptation. In: GECCO 2006: Proceedings of the 8th annual conference on Genetic and evolutionary computation, pp. 1505–1512. ACM Press, New York (2006)
Lanzi, P.L., Loiacono, D., Wilson, S.W., Goldberg, D.E.: Generalization in the XCSF classifier system: Analysis, improvement, and extension. Evolutionary Computation 15(2), 133–168 (2007)
Loiacono, D., Marelli, A., Lanzi, P.L.: Support vector regression for classifier prediction. In: GECCO 2007: Proceedings of the 9th annual conference on Genetic and evolutionary computation, pp. 1806–1813. ACM Press, New York (2007)
Weisstein, E.W.: Sherman-morrison formula. From MathWorld–A Wolfram Web Resource, http://mathworld.wolfram.com/Sherman-MorrisonFormula.html
Widrow, B., Hoff, M.E.: Neurocomputing: Foundation of Research. In: Adaptive Switching Circuits, pp. 126–134. MIT Press, Cambridge (1988)
Wilson, S.W.: Classifier Fitness Based on Accuracy. Evolutionary Computation 3(2), 149–175 (1995)
Wilson, S.W.: Classifiers that approximate functions. Journal of Natural Computing 1(2-3), 211–234 (2002)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2008 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Loiacono, D., Drugowitsch, J., Barry, A., Lanzi, P.L. (2008). Analysis and Improvements of the Classifier Error Estimate in XCSF. In: Bacardit, J., Bernadó-Mansilla, E., Butz, M.V., Kovacs, T., Llorà, X., Takadama, K. (eds) Learning Classifier Systems. IWLCS IWLCS 2006 2007. Lecture Notes in Computer Science(), vol 4998. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-88138-4_7
Download citation
DOI: https://doi.org/10.1007/978-3-540-88138-4_7
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-88137-7
Online ISBN: 978-3-540-88138-4
eBook Packages: Computer ScienceComputer Science (R0)