Improving the Performance of Fuzzy Rule Based Classification Systems for Highly Imbalanced Data-Sets Using an Evolutionary Adaptive Inference System

In this contribution, we study the influence of an Evolutionary Adaptive Inference System with parametric conjunction operators for Fuzzy Rule Based Classification Systems. Specifically, we work in the context of highly imbalanced data-sets, which is a common scenario in real applications, since the number of examples that represents one of the classes of the data-set (usually the concept of interest) is usually much lower than that of the other classes. Our experimental study shows empirically that the use of the parametric conjunction operators enables simple Fuzzy Rule Based Classification Systems to enhance their performance for data-sets with a high imbalance ratio.