Characterizing Quantifier Fuzzification Mechanisms: A behavioral guide for applications

dc.contributor.affiliationUniversidade de Santiago de Compostela. Centro de Investigación en Tecnoloxías da Informacióngl
dc.contributor.affiliationUniversidade de Santiago de Compostela. Departamento de Electrónica e Computacióngl
dc.contributor.affiliationUniversidade de Santiago de Compostela. Departamento de Filosofía e Antropoloxíagl
dc.contributor.areaÁrea de Enxeñaría e Arquitectura
dc.contributor.authorDíaz Hermida, Félix
dc.contributor.authorPereira Fariña, Martín
dc.contributor.authorVidal Aguiar, Juan Carlos
dc.contributor.authorRamos Soto, Alejandro
dc.date.accessioned2019-02-25T12:08:57Z
dc.date.available2019-07-25T01:00:09Z
dc.date.issued2018-08-15
dc.description.abstractImportant advances have been made in the fuzzy quantification field. Nevertheless, some problems remain when we face the decision of selecting the most convenient model for a specific application. In the literature, several desirable adequacy properties have been proposed, but theoretical limits impede quantification models from simultaneously fulfilling every adequacy property that has been defined. Besides, the complexity of model definitions and adequacy properties makes very difficult for real users to understand the particularities of the different models that have been presented. In this work we will present several criteria conceived to help in the process of selecting the most adequate Quantifier Fuzzification Mechanisms for specific practical applications. In addition, some of the best known well-behaved models will be compared against this list of criteria. Based on this analysis, some guidance to choose fuzzy quantification models for practical applications will be providedgl
dc.description.peerreviewedSIgl
dc.description.sponsorshipThis work was supported by the Spanish Ministry of Economy and Competitiveness under the project TIN2015-73566-JIN and by the European Regional Development Fund (ERDF/FEDER), the Consellería de Cultura, Educación e Ordenación Universitaria (accreditation 2016–2019, ED431G/08, reference competitive group 2014–2017, GRC2014/030, Postdoctoral Training Grants 2016), and the European Regional Development Fund (ERDF)gl
dc.identifier.citationDiaz-Hermida, F., Pereira-Fariña, M., Vidal, J., Ramos-Soto, A. (2018) Characterizing Quantifier Fuzzification Mechanisms: A behavioral guide for applications. Fuzzy Sets And Systems, 345: 1-23. Doi: 10.1016/j.fss.2017.07.017gl
dc.identifier.doi10.1016/j.fss.2017.07.017
dc.identifier.issn0165-0114
dc.identifier.urihttp://hdl.handle.net/10347/18302
dc.language.isoenggl
dc.publisherElseviergl
dc.relation.projectIDinfo:eu-repo/grantAgreement/MINECO/Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016/TIN2015-73566-JIN/ES/SOFT COMPUTING PARA ANALITICAS DE GAMIFICACION EN REHABILITACION CARDIACA
dc.relation.publisherversionhttps://doi.org/10.1016/j.fss.2017.07.017gl
dc.rights© 2017, Elsevier. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/gl
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 Internacional
dc.rights.accessRightsopen accessgl
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectFuzzy quantificationgl
dc.subjectDeterminer fuzzification schemesgl
dc.subjectTheory of generalized quantifiersgl
dc.subjectQuantifier Fuzzification Mechanismgl
dc.subjectApplications of fuzzy quantificationgl
dc.titleCharacterizing Quantifier Fuzzification Mechanisms: A behavioral guide for applicationsgl
dc.typejournal articlegl
dc.type.hasVersionAMgl
dspace.entity.typePublication
relation.isAuthorOfPublication0150b339-bec0-4820-a75b-ebb1da27d8dc
relation.isAuthorOfPublication3e3bbb70-0c93-4f28-84a7-3f66aca264b8
relation.isAuthorOfPublicationfc38e5db-7f42-4fc2-8506-3c6ac86dbfa2
relation.isAuthorOfPublication.latestForDiscovery0150b339-bec0-4820-a75b-ebb1da27d8dc

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