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.
Keywords
- 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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© 2008 Springer-Verlag Berlin Heidelberg
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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
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DOI: https://doi.org/10.1007/978-3-540-88138-4_7
Publisher Name: Springer, Berlin, Heidelberg
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