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dc.creatorSanz Delgado, José Antonioes_ES
dc.creatorFernández, Albertoes_ES
dc.creatorBustince Sola, Humbertoes_ES
dc.creatorHerrera, Franciscoes_ES
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 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.en
dc.description.sponsorshipThis work has been supported by the Spanish Ministry of Science and Technology under projects TIN2008-06681-C06-01 and TIN2007-65981.en
dc.relation.ispartofInformation Sciences 180 (2010) 3674–3685en
dc.rights© 2010 Elsevier Inc. The manuscript version is made available under the CC BY-NC-ND 4.0 licenseen
dc.subjectFuzzy rule-based classification systemsen
dc.subjectInterval-valued fuzzy setsen
dc.subjectGenetic algorithmsen
dc.titleImproving the performance of fuzzy rule-based classification systems with interval-valued fuzzy sets and genetic amplitude tuningen
dc.typeArtículo / Artikuluaes
dc.contributor.departmentUniversidad Pública de Navarra. Departamento de Automática y Computaciónes_ES
dc.contributor.departmentNafarroako Unibertsitate Publikoa. Automatika eta Konputazioa Sailaeu
dc.rights.accessRightsAcceso abierto / Sarbide irekiaes
dc.type.versionVersión aceptada / Onetsi den bertsioaes

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© 2010 Elsevier Inc. The manuscript version is made available under the CC BY-NC-ND 4.0 license
Except where otherwise noted, this item's license is described as © 2010 Elsevier Inc. The manuscript version is made available under the CC BY-NC-ND 4.0 license