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dc.creatorUriz Martín, Mikel Xabieres_ES
dc.creatorPaternain Dallo, Danieles_ES
dc.creatorDomínguez Catena, Irises_ES
dc.creatorBustince Sola, Humbertoes_ES
dc.creatorGalar Idoate, Mikeles_ES
dc.date.accessioned2020-07-03T12:54:09Z
dc.date.available2020-07-03T12:54:09Z
dc.date.issued2020
dc.identifier.citationM. Uriz, D. Paternain, I. Dominguez-Catena, H. Bustince and M. Galar, 'Unsupervised Fuzzy Measure Learning for Classifier Ensembles From Coalitions Performance,' in IEEE Access, vol. 8, pp. 52288-52305, 2020, doi: 10.1109/ACCESS.2020.2980949.en
dc.identifier.issn2169-3536
dc.identifier.urihttps://hdl.handle.net/2454/37293
dc.description.abstractIn Machine Learning an ensemble refers to the combination of several classifiers with the objective of improving the performance of every one of its counterparts. To design an ensemble two main aspects must be considered: how to create a diverse set of classifiers and how to combine their outputs. This work focuses on the latter task. More specifically, we focus on the usage of aggregation functions based on fuzzy measures, such as the Sugeno and Choquet integrals, since they allow to model the coalitions and interactions among the members of the ensemble. In this scenario the challenge is how to construct a fuzzy measure that models the relations among the members of the ensemble. We focus on unsupervised methods for fuzzy measure construction, review existing alternatives and categorize them depending on their features. Furthermore, we intend to address the weaknesses of previous alternatives by proposing a new construction method that obtains the fuzzy measure directly evaluating the performance of each possible subset of classifiers, which can be efficiently computed. To test the usefulness of the proposed fuzzy measure, we focus on the application of ensembles for imbalanced datasets. We consider a set of 66 imbalanced datasets and develop a complete experimental study comparing the reviewed methods and our proposal.en
dc.description.sponsorshipThis work was supported in part by the Spanish Ministry of Science and Technology (AEI/FEDER, UE) under Project TIN2016-77356-P, and in part by the Public University of Navarra under Project PJUPNA13.en
dc.format.extent18 p.
dc.format.mimetypeapplication/pdfen
dc.language.isoengen
dc.publisherIEEEen
dc.relation.ispartofIEEE Access, 2020, 8, 52288-52305en
dc.rightsThis work is licensed under a Creative Commons Attribution 4.0 License.en
dc.subjectFuzzy measuresen
dc.subjectChoquet integralen
dc.subjectAggregationen
dc.subjectEnsemblesen
dc.subjectClassificationen
dc.titleUnsupervised fuzzy measure learning for classifier ensembles from coalitions performanceen
dc.typeinfo:eu-repo/semantics/articleen
dc.typeArtículo / Artikuluaes
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/ACCESS.2020.2980949
dc.relation.projectIDinfo:eu-repo/grantAgreement/ES/1PE/TIN2016-77356-Pen
dc.relation.publisherversionhttps://doi.org/10.1109/ACCESS.2020.2980949
dc.type.versioninfo:eu-repo/semantics/publishedVersionen
dc.type.versionVersión publicada / Argitaratu den bertsioaes
dc.contributor.funderUniversidad Pública de Navarra / Nafarroako Unibertsitate Publikoa, PJUPNA13es


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