An experiment with fuzzy sets in data mining

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Abstract

Fuzzy modeling provides a very useful tool to deal with human vagueness in describing scales of value. This study examines the relative error in decision tree models applied to a real set of credit card data used in the literature, comparing crisp models with fuzzy decision trees as applied by See5, and as obtained by categorization of data. The impact of ordinal data is also tested. Modifying continuous data was expected to degrade model accuracy, but was expected to be more robust with respect to human understanding. The degree of accuracy lost by See5 fuzzification was minimal (in fact more accurate in terms of total error), although bad error was worse. Categorization of data yielded greater inaccuracy. However, both treatments are still useful if they better reflect human understanding. An additional conclusion is that when categorizing data, care should be taken in setting categorical limits. © Springer-Verlag Berlin Heidelberg 2007.

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APA

Olson, D. L., Moshkovich, H., & Mechitov, A. (2007). An experiment with fuzzy sets in data mining. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 4488 LNCS, pp. 462–469). Springer Verlag. https://doi.org/10.1007/978-3-540-72586-2_66

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