Abstract
An algorithm is presented for reducing the size of evolved classifier populations. On the Wisconsin Breast Cancer dataset, the algorithm produced compact rulesets substantially smaller than the populations, yet performance in cross-validation tests was nearly unchanged. Classifiers of the rulesets expressed readily interpretable knowledge about the dataset that should be useful to practitioners.
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Wilson, S.W. (2002). Compact Rulesets from XCSI. In: Lanzi, P.L., Stolzmann, W., Wilson, S.W. (eds) Advances in Learning Classifier Systems. IWLCS 2001. Lecture Notes in Computer Science(), vol 2321. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-48104-4_12
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DOI: https://doi.org/10.1007/3-540-48104-4_12
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