Improving the Performance of Fuzzy Rule-Based Classification Systems with Interval-Valued Fuzzy Sets and Genetic Amplitude Tuning Sanz, José Antonio Fernández, Alberto Bustince, Humberto Herrera Triguero, Francisco Fuzzy rule based classification systems IntervalValued Fuzzy Sets Tuning Genetic algorithms Among 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 IntervalValued 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. 2020-12-17T09:57:53Z 2020-12-17T09:57:53Z 2010 info:eu-repo/semantics/article Antonio Sanz, J., Fernandez, A., Bustince, H., & Herrera, F. (2010). Improving the performance of fuzzy rule-based classification systems with interval-valued fuzzy sets and genetic amplitude tuning. Information Sciences, 180(19), 3674-3685. doi:10.1016/j.ins.2010.06.018 http://hdl.handle.net/10481/64975 10.1016/j.ins.2010.06.018 eng http://creativecommons.org/licenses/by-nc-nd/3.0/es/ info:eu-repo/semantics/openAccess Atribución-NoComercial-SinDerivadas 3.0 España ELSEVIER