Please use this identifier to cite or link to this item: http://repositorio.unicamp.br/jspui/handle/REPOSIP/341648
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dc.contributor.CRUESPUNIVERSIDADE ESTADUAL DE CAMPINASpt_BR
dc.contributor.authorunicampAyres, Amanda Ortega de Castro-
dc.contributor.authorunicampVon Zuben, Fernando José-
dc.typeArtigopt_BR
dc.titleMultitask learning applied to evolving fuzzy-rule-based predictorspt_BR
dc.contributor.authorAyres, Amanda O. C.-
dc.contributor.authorVon Zuben, Fernando J.-
dc.subjectSistemas fuzzypt_BR
dc.subject.otherlanguageFuzzy systemspt_BR
dc.description.abstractThis paper converts the online learning framework denoted FBeM (Fuzzy set Based evolving Modeling) to an online multitask learning evolving system. FBeM is data flow driven and recursively updates information granules that can be interpreted as the antecedent parts of functional IF-THEN fuzzy rules. The intersection of those information granules is directly interpreted here as defining a sparse graph of structural relationships among the IF-THEN fuzzy rules, so that the consequent terms of the rules, corresponding to linear regression models, can be produced by the solution of a regularized multitask learning problem. Being an evolving system, every time that the information granules are updated, including possibly merging and splitting of existing information granules, the multitask learning step should be re-executed, thus motivating our investment on scalability issues such as sparsity of the graph and convexity of the regularized formulation. Weather temperature and wind speed in eolian farms are taken as the two case studies devoted to online time series prediction. When compared to the original FBeM framework, which treats the learning of the regression models as independent tasks, and also to several other state-of-the-art evolving systems in the literature, our approach guides to an expressive gain in performance in most cases, even consistently resorting to a reduced set of IF-THEN fuzzy rules to synthesize the online predictorspt_BR
dc.relation.ispartofEvolving systems: an interdisciplinary journal for advanced science and technologypt_BR
dc.publisher.cityHeidelbergpt_BR
dc.publisher.countryAlemanhapt_BR
dc.publisherSpringerpt_BR
dc.date.issued2019-
dc.date.monthofcirculationAug.pt_BR
dc.language.isoengpt_BR
dc.rightsFechadopt_BR
dc.sourceScopuspt_BR
dc.identifier.issn1868-6478pt_BR
dc.identifier.eissn1868-6486pt_BR
dc.identifier.doi10.1007/s12530-019-09300-wpt_BR
dc.identifier.urlhttps://link.springer.com/article/10.1007/s12530-019-09300-wpt_BR
dc.description.sponsorshipCONSELHO NACIONAL DE DESENVOLVIMENTO CIENTÍFICO E TECNOLÓGICO - CNPQpt_BR
dc.description.sponsordocumentnumber#143455/ 2017-6; #307228/2018-5pt_BR
dc.date.available2020-05-18T23:44:49Z-
dc.date.accessioned2020-05-18T23:44:49Z-
dc.description.provenanceSubmitted by Thais de Brito Barroso (tbrito@unicamp.br) on 2020-05-18T23:44:49Z No. of bitstreams: 0en
dc.description.provenanceMade available in DSpace on 2020-05-18T23:44:49Z (GMT). No. of bitstreams: 0 Previous issue date: 2019en
dc.identifier.urihttp://repositorio.unicamp.br/jspui/handle/REPOSIP/341648-
dc.contributor.departmentSem informaçãopt_BR
dc.contributor.departmentDepartamento de Engenharia de Computação e Automação Industrialpt_BR
dc.contributor.unidadeFaculdade de Engenharia Elétrica e de Computaçãopt_BR
dc.contributor.unidadeFaculdade de Engenharia Elétrica e de Computaçãopt_BR
dc.subject.keywordEvolving fuzzy-rule-based systemspt_BR
dc.subject.keywordOnline learningpt_BR
dc.subject.keywordMultitask learningpt_BR
dc.subject.keywordTime series predictionpt_BR
dc.identifier.source2-s2.0-85071436853pt_BR
dc.creator.orcidSem informaçãopt_BR
dc.creator.orcid0000-0002-4128-5415pt_BR
dc.type.formArtigo originalpt_BR
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