Abstract
Some numerical attributes may be reduced during discretization. It happens when a discretized attribute has only one interval, i.e., the entire domain of a numerical attribute is mapped into a single interval. The problem is how such reduction of data sets affects the error rate measured by the C4.5 decision tree generation system using ten-fold cross-validation . Our experiments on 15 numerical data sets show that for a Dominant Attribute discretization method the error rate is significantly larger (5% significance level, two-tailed test ) for the reduced data sets. However, decision trees generated from the reduced data sets are significantly simpler than the decision trees generated from the original data sets.
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Grzymała-Busse, J.W., Mroczek, T. (2018). Attribute Selection Based on Reduction of Numerical Attributes During Discretization. In: Stańczyk, U., Zielosko, B., Jain, L. (eds) Advances in Feature Selection for Data and Pattern Recognition. Intelligent Systems Reference Library, vol 138. Springer, Cham. https://doi.org/10.1007/978-3-319-67588-6_2
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DOI: https://doi.org/10.1007/978-3-319-67588-6_2
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