A study on the use of the fuzzy reasoning method based on the winning rule vs. Voting procedure for classification with imbalanced data sets

Abstract

In this contribution we carry out an analysis of the Fuzzy Reasoning Methods for Fuzzy Rule Based Classification Systems in the framework of balanced and imbalanced data-sets with different degrees of imbalance. We analyze the behaviour of the Fuzzy Rule Based Classification Systems searching for the best type of Fuzzy Reasoning Method in each case, also studying the cooperation of some pre-processing methods of instances for imbalanced data-sets. To do so we use a fuzzy rule learning method that extends the well-known Wang and Mendel algorithm to classification problems. The results obtained show the necessity to apply an instance preprocessing step and the differences for the most appropriate Fuzzy Reasoning Method in balanced and imbalanced data-sets, concluding that the choice of the Fuzzy Reasoning Method depends on the degree of imbalance, being the most adequate the use of the Winning Rule for high imbalanced data-sets and the Additive Combination method for the remaining data-sets. © Springer-Verlag Berlin Heidelberg 2007.