Sanz Delgado, José AntonioFernández, AlbertoBustince Sola, HumbertoHerrera, Francisco2015-07-272015-07-2720100020-025510.1016/j.ins.2010.06.018https://academica-e.unavarra.es/handle/2454/17683Among the computational intelligence techniques employed to solve classification problems, Fuzzy Rule-Based Classification Systems (FRBCSs) are a popular tool because of their interpretable models based on linguistic variables, which are easier to understand for the experts or end-users. The aim of this paper is to enhance the performance of FRBCSs by extending the Knowledge Base with the application of the concept of Interval-Valued Fuzzy Sets (IVFSs). We consider a post-processing genetic tuning step that adjusts the amplitude of the upper bound of the IVFS to contextualize the fuzzy partitions and to obtain a most accurate solution to the problem. We analyze the goodness of this approach using two basic and well-known fuzzy rule learning algorithms, the Chi et al.’s method and the fuzzy hybrid genetics-based machine learning algorithm. We show the improvement achieved by this model through an extensive empirical study with a large collection of data-sets.application/pdfeng© 2010 Elsevier Inc. The manuscript version is made available under the CC BY-NC-ND 4.0 licenseFuzzy rule-based classification systemsInterval-valued fuzzy setsTuningGenetic algorithmsinfo:eu-repo/semantics/articleinfo:eu-repo/semantics/openAccess