A genetic algorithm for feature selection and granularity learning in fuzzy rule-based classification systems for highly imbalanced data-sets

Abstract

This contribution proposes a Genetic Algorithm for jointly performing a feature selection and granularity learning for Fuzzy Rule-Based Classification Systems in the scenario of data-sets with a high imbalance degree. We refer to imbalanced data-sets when the class distribution is not uniform, a situation that it is present in many real application areas. The aim of this work is to get more compact and precise models by selecting the adequate variables and adapting the number of fuzzy labels for each problem. © Springer-Verlag Berlin Heidelberg 2010.