Cost-Sensitive Learning of Fuzzy Rules for Imbalanced Classification Problems Using FURIA

This paper is intended to verify that cost-sensitive learning is a competitive approach for learning fuzzy rules in certain imbalanced classification problems. It will be shown that there exist cost matrices whose use in combination with a suitable classifier allows for improving the results of some popular data-level techniques. The well known FURIA algorithm is extended to take advantage of this definition. A numerical study is carried out to compare the proposed cost-sensitive FURIA to other state-of-the-art classification algorithms, based on fuzzy rules and on other classical machine learning methods, on 64 different imbalanced datasets.