Abstract
This paper compares performance of the Pittsburgh-style system GAssist with the Michigan-style system XCS on several datamining problems. Our analysis shows that both systems are suitable for datamining but have different advantages and disadvantages. The study does not only reveal important differences between the two systems but also suggests several structural properties of the underlying datasets.
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Bacardit, J., Butz, M.V. (2007). Data Mining in Learning Classifier Systems: Comparing XCS with GAssist. 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_19
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DOI: https://doi.org/10.1007/978-3-540-71231-2_19
Publisher Name: Springer, Berlin, Heidelberg
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