A Short Study on the Use of Genetic 2-Tuples Tuning for Fuzzy Rule Based Classification Systems in Imbalanced Data-Sets

Abstract

In this work our aim is to increase the performance of Fuzzy Rule Based Classifications Systems in the framework of imbalanced data-sets by means of the application of a genetic tuning step. We focus on the imbalanced data-set problem since it appears in many real application areas and, for this reason, it has become a relevant topic in the area of machine learning. This problem occurs when the number of examples that represents one of the concepts of interest (usually the most important) is much lower than that of the remaining ones. We want to adapt the 2-tuples based genetic tuning approach to classification problems and to study the positive synergy between this method and the Chi et al.'s fuzzy learning method, which is a basic approach in order to build the initial Knowledge Base. The experimental results show the improvement achieved by the 2-tuples based genetic tuning over the Fuzzy Rule Based Classification System in all types of imbalanced data, obtaining a better behaviour than the basic approach.