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. © Springer-Verlag Berlin Heidelberg 2007.
Cite
CITATION STYLE
Bacardit, J., & Butz, M. V. (2007). Data mining in learning classifier systems: Comparing XCS with GAssist. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 4399 LNAI, pp. 282–290). Springer Verlag. https://doi.org/10.1007/978-3-540-71231-2_19
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