Barrenechea Tartas, EdurneBustince Sola, HumbertoFernández Fernández, Francisco JavierPaternain Dallo, DanielSanz Delgado, José Antonio2015-07-272015-07-2720132075-168010.3390/axioms2020208https://academica-e.unavarra.es/handle/2454/17687In this paper we present a new fuzzy reasoning method in which the Choquet integral is used as aggregation function. In this manner, we can take into account the interaction among the rules of the system. For this reason, we consider several fuzzy measures, since it is a key point on the subsequent success of the Choquet integral, and we apply the new method with the same fuzzy measure for all the classes. However, the relationship among the set of rules of each class can be different and therefore the best fuzzy measure can change depending on the class. Consequently, we propose a learning method by means of a genetic algorithm in which the most suitable fuzzy measure for each class is computed. From the obtained results it is shown that our new proposal allows the performance of the classical fuzzy reasoning methods of the winning rule and additive combination to be enhanced whenever the fuzzy measure is appropriate for the tackled problem.application/pdfeng© 2013 by the authors; licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution license.Fuzzy rule-based classification systemsChoquet integralFuzzy measureGenetic algorithminfo:eu-repo/semantics/articleAcceso abierto / Sarbide irekiainfo:eu-repo/semantics/openAccess