Crisp classifiers vs. fuzzy classifiers: a statistical study

Authors:
J. L. Jara;Rodrigo Acevedo-Crespo
Affiliations:
Universidad de Santiago de Chile, Depto. de Ingeniería Informática, Santiago, Chile;Universidad de Santiago de Chile, Depto. de Ingeniería Informática, Santiago, Chile
Venue:
ICANNGA'09 Proceedings of the 9th international conference on Adaptive and natural computing algorithms
Year:
2009

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Abstract

A study is made of whether there is a significant statistical difference in performance between crisp and fuzzy rule-based classification. To do that, 12 datasets were chosen from the UCI repository that are widely used in the literature, and use was made of four different algorithms for rule induction --two crisp and two fuzzy-- to classify them. Then a non-parametric statistical test was used for measuring the significance of the results, which indicated that both paradigms --crisp and fuzzy classification-- are not different in the statistical meaning.