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 pre-processing 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.
Keywords Fuzzy Rule Based Classification Systems - Over-sampling - Imbalanced Data-sets - rule weight - Fuzzy Reasoning Method