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dc.creatorElkano Ilintxeta, Mikeles_ES
dc.creatorSanz Delgado, José Antonioes_ES
dc.creatorBarrenechea Tartas, Edurnees_ES
dc.creatorBustince Sola, Humbertoes_ES
dc.creatorGalar Idoate, Mikeles_ES
dc.date.accessioned2020-05-20T08:33:23Z
dc.date.available2020-05-20T08:33:23Z
dc.date.issued2020
dc.identifier.citationM. Elkano, J. A. Sanz, E. Barrenechea, H. Bustince and M. Galar, 'CFM-BD: A Distributed Rule Induction Algorithm for Building Compact Fuzzy Models in Big Data Classification Problems,' in IEEE Transactions on Fuzzy Systems, vol. 28, no. 1, pp. 163-177, Jan. 2020, doi: 10.1109/TFUZZ.2019.2900856.en
dc.identifier.issn1063-6706
dc.identifier.urihttps://hdl.handle.net/2454/36932
dc.description.abstractInterpretability has always been a major concern for fuzzy rule-based classifiers. The usage of human-readable models allows them to explain the reasoning behind their predictions and decisions. However, when it comes to Big Data classification problems, fuzzy rule based classifiers have not been able to maintain the good tradeoff between accuracy and interpretability that has characterized these techniques in non-Big-Data environments. The most accurate methods build models composed of a large number of rules and fuzzy sets that are too complex, while those approaches focusing on interpretability do not provide state-of-the-art discrimination capabilities. In this paper, we propose a new distributed learning algorithm named CFM-BD to construct accurate and compact fuzzy rule-based classification systems for Big Data. This method has been specifically designed from scratch for Big Data problems and does not adapt or extend any existing algorithm. The proposed learning process consists of three stages: Preprocessing based on the probability integral transform theorem; rule induction inspired by CHI-BD and Apriori algorithms; and rule selection by means of a global evolutionary optimization. We conducted a complete empirical study to test the performance of our approach in terms of accuracy, complexity, and runtime. The results obtained were compared and contrasted with four state-of-the-art fuzzy classifiers for Big Data (FBDT, FMDT, Chi-Spark-RS, and CHI-BD). According to this study, CFM-BD is able to provide competitive discrimination capabilities using significantly simpler models composed of a few rules of less than three antecedents, employing five linguistic labels for all variables.en
dc.description.sponsorshipThis work has been supported by the Spanish Ministry of Economy and Competitiveness under the project TIN2016-77356-P (MINECO, AEI/FEDER, UE).en
dc.format.extent20 p.
dc.format.mimetypeapplication/pdfen
dc.language.isoengen
dc.publisherIEEEen
dc.relation.ispartofIEEE Transactions on Fuzzy Systems, 2020, 28 (1), 163-177en
dc.rights© 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other worken
dc.subjectApache sparken
dc.subjectBig dataen
dc.subjectEvolutionary algorithmsen
dc.subjectFuzzy rule based classification systems (FRBCSs)en
dc.subjectProbability integral transformen
dc.subjectQuantile functionen
dc.titleCFM-BD: a distributed rule induction algorithm for building compact fuzzy models in Big Data classification problemsen
dc.typeinfo:eu-repo/semantics/articleen
dc.typeArtículo / Artikuluaes
dc.contributor.departmentUniversidad Pública de Navarra. Departamento de Estadística, Informática y Matemáticases_ES
dc.contributor.departmentNafarroako Unibertsitate Publikoa. Estatistika, Informatika eta Matematika Sailaeu
dc.contributor.departmentUniversidad Pública de Navarra / Nafarroako Unibertsitate Publikoa. ISC - Institute of Smart Citieses_ES
dc.rights.accessRightsinfo:eu-repo/semantics/openAccessen
dc.rights.accessRightsAcceso abierto / Sarbide irekiaes
dc.identifier.doi10.1109/TFUZZ.2019.2900856
dc.relation.projectIDinfo:eu-repo/grantAgreement/ES/1PE/TIN2016-77356-Pen
dc.relation.publisherversionhttps://doi.org/10.1109/TFUZZ.2019.2900856
dc.type.versioninfo:eu-repo/semantics/acceptedVersionen
dc.type.versionVersión aceptada / Onetsi den bertsioaes


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