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The Effect of Imbalanced Data Class Distribution on Fuzzy Classifiers - Experimental Study

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2 Author(s)
Visa, S. ; Dept. of Electr. Comput. & Eng. Comput. Sci., Cincinnati Univ., OH ; Ralescu, A.

This study evaluates the robustness of a fuzzy classifier when class distribution of the training set varies. The analysis of the results is based on the classification accuracy and ROC curves. The experimental results reported here show that fuzzy classifiers are less variant with the class distribution and less sensitive to the imbalance factor than decision trees

Published in:

Fuzzy Systems, 2005. FUZZ '05. The 14th IEEE International Conference on

Date of Conference:

25-25 May 2005

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