The Effect of Imbalanced Data Class Distribution on Fuzzy Classifiers - Experimental Study
Sofia Visa, Anca Ralescu · 2005
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