Abstract
XCS is a stochastic algorithm, so it does not guarantee to produce the same results when run with the same input. When interpretability matters, obtaining a single, stable result is important. We propose an algorithm which applies clustering in order to merge the rules produced from many XCS runs. Such an algorithm needs a measure of distance between rules; we then suggest a general definition for such a measure. We finally evaluate the results obtained on two well-known data sets, with respect to performance and stability. We find that stability is improved, while performance is slightly impaired.
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Baronti, F., Passaro, A., Starita, A. (2007). Post-processing Clustering to Decrease Variability in XCS Induced Rulesets. In: Kovacs, T., Llorà, X., Takadama, K., Lanzi, P.L., Stolzmann, W., Wilson, S.W. (eds) Learning Classifier Systems. IWLCS IWLCS IWLCS 2003 2004 2005. Lecture Notes in Computer Science(), vol 4399. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-71231-2_6
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DOI: https://doi.org/10.1007/978-3-540-71231-2_6
Publisher Name: Springer, Berlin, Heidelberg
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