A fuzzy association rule-based classifier for imbalanced classification problems

Imbalanced classification problems are attracting the attention of the research community because they are prevalent in real-world problems and they impose extra difficulties for learning methods. Fuzzy rule-based classification systems have been applied to cope with these problems, mostly together with sampling techniques. In this paper, we define a new fuzzy association rule-based classifier, named FARCI, to tackle directly imbalanced classifi-cation problems. Our new proposal belongs to the algorithm modification category, since it is constructed on the basis of the state-of-the-art fuzzy classifier FARC-HD. Specifically, we modify its three learning stages, aiming at boosting the number of fuzzy rules of the minor -ity class as well as simplifying them and, for the sake of handling unequal fuzzy rule lengths, we also change the matching degree computation, which is a key step of the infer-ence process and it is also involved in the learning process. In the experimental study, we analyze the effectiveness of each one of the new components in terms of performance, F -score, and rule base size. Moreover, we also show the superiority of the new method when compared versus FARC-HD alongside sampling techniques, another algorithm mod-ification approach, two cost-sensitive methods and an ensemble. (c) 2021 The Authors. Published by Elsevier Inc. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).