An analysis of the rule weights and fuzzy reasoning methods for linguistic rule based classification systems applied to problems with highly imbalanced data sets

In this contribution we carry out an analysis of the rule weights and Fuzzy Reasoning Methods for Fuzzy Rule Based Classification Systems in the framework of imbalanced data-sets with a high imbalance degree. We analyze the behaviour of the Fuzzy Rule Based Classification Systems searching for the best configuration of rule weight and Fuzzy Reasoning Method also studying the cooperation of some pre-processing methods of instances. To do so we use a simple rule base obtained with the Chi (and co-authors') method that extends the well-known Wang and Mendel method to classification problems. The results obtained show the necessity to apply an instance preprocessing step and the clear differences in the use of the rule weight and Fuzzy Reasoning Method. Finally, it is empirically proved that there is a superior performance of Fuzzy Rule Based Classification Systems compared to the 1-NN and C4.5 classifiers in the framework of highly imbalanced data-sets.