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dc.contributor.authorSanz, José Antonio
dc.contributor.authorFernández, Alberto
dc.contributor.authorBustince, Humberto
dc.contributor.authorHerrera Triguero, Francisco 
dc.date.accessioned2020-12-17T09:57:53Z
dc.date.available2020-12-17T09:57:53Z
dc.date.issued2010
dc.identifier.citationAntonio 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.2010.06.018es_ES
dc.identifier.urihttp://hdl.handle.net/10481/64975
dc.description.abstractAmong 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.es_ES
dc.description.sponsorshipSpanish Government TIN2008-06681-C06-01 TIN2007-65981es_ES
dc.language.isoenges_ES
dc.publisherELSEVIERes_ES
dc.rightsAtribución-NoComercial-SinDerivadas 3.0 España*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/es/*
dc.subjectFuzzy rule based classification systemses_ES
dc.subjectIntervalValued Fuzzy Setses_ES
dc.subjectTuning es_ES
dc.subjectGenetic algorithmses_ES
dc.titleImproving the Performance of Fuzzy Rule-Based Classification Systems with Interval-Valued Fuzzy Sets and Genetic Amplitude Tuninges_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.rights.accessRightsinfo:eu-repo/semantics/openAccesses_ES
dc.identifier.doi10.1016/j.ins.2010.06.018


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Atribución-NoComercial-SinDerivadas 3.0 España
Except where otherwise noted, this item's license is described as Atribución-NoComercial-SinDerivadas 3.0 España