Abstract
The classifier system XCS was investigated for data mining applications where the dataset discrimination surface (DS) is generally oblique to the attribute axes. Despite the classifiers’ hyper-rectangular predicates, XCS reached 100% performance on synthetic problems with diagonal DS’s and, in a train/test experiment, competitive performance on the Wisconsin Breast Cancer dataset. Final classifiers in an extended WBC learning run were interpretable to suggest dependencies on one or a few attributes. For data mining of numeric datasets with partially oblique discrimination surfaces, XCS shows promise from both performance and pattern discovery viewpoints.
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Wilson, S.W. (2001). Mining Oblique Data with XCS. In: Luca Lanzi, P., Stolzmann, W., Wilson, S.W. (eds) Advances in Learning Classifier Systems. IWLCS 2000. Lecture Notes in Computer Science(), vol 1996. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44640-0_11
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DOI: https://doi.org/10.1007/3-540-44640-0_11
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