Sanz Delgado, José AntonioDa Cruz Asmus, TiagoOsa Hernández, Borja de laBustince Sola, Humberto2021-04-152021-06-052020978-3-030-50153-2 (Electronic)10.1007/978-3-030-50153-2_35https://academica-e.unavarra.es/handle/2454/39550Trabajo presentado a la 18th International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems, IPMU 2020 (15-19 de junio de 2020)Interval-Valued fuzzy rule-based classifier with TUning and Rule Selection, IVTURS, is a state-of-the-art fuzzy classifier. One of the key point of this method is the usage of interval-valued restricted equivalence functions because their parametrization allows one to tune them to each problem, which leads to obtaining accurate results. However, they require the application of the exponentiation several times to obtain a result, which is a time demanding operation implying an extra charge to the computational burden of the method. In this contribution, we propose to reduce the number of exponentiation operations executed by the system, so that the efficiency of the method is enhanced with no alteration of the obtained results. Moreover, the new approach also allows for a reduction on the search space of the evolutionary method carried out in IVTURS. Consequently, we also propose four different approaches to take advantage of this reduction on the search space to study if it can imply an enhancement of the accuracy of the classifier. The experimental results prove: 1) the enhancement of the efficiency of IVTURS and 2) the accuracy of IVTURS is competitive versus that of the approaches using the reduced search space.14 p.application/pdfeng© Springer Nature Switzerland AG 2020Interval-valued fuzzy rule-based classification systemsInterval-valued fuzzy setsInterval type-2 fuzzy setsEvolutionary fuzzy systemsinfo:eu-repo/semantics/conferenceObjectinfo:eu-repo/semantics/openAccess