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dc.creatorBueno, Jéssica C. S.es_ES
dc.creatorDias, Camila A.es_ES
dc.creatorPereira Dimuro, Graçalizes_ES
dc.creatorSantos, Helidaes_ES
dc.creatorBustince Sola, Humbertoes_ES
dc.date.accessioned2021-03-05T11:32:50Z
dc.date.available2021-03-05T11:32:50Z
dc.date.issued2019
dc.identifier.isbn978-94-6252-770-6
dc.identifier.issn2589-6644
dc.identifier.urihttps://hdl.handle.net/2454/39349
dc.description.abstractThe rising volume of data and its high complexity has brought the need of developing increasingly efficient knowledge extraction techniques, which demands efficiency both in computational cost and in accuracy. Most of problems that are handled by these techniques has complex information to be identified. So, machine learning methods are frequently used, where a variety of functions can be applied in the different steps that are employed in their architecture. One of them is the use of aggregation functions aiming at resizing images. In this context, we introduce a study of aggregation functions based on the Choquet integral, whose main characteristic in comparison with other aggregation functions is that it considers, through fuzzy measure, the interaction between the elements to be aggregated. Thus, our main goal is to present an evaluation study of the performance of the standard Choquet integral the and copula-based generalization of the Choquet integral in relation to the maximum and mean functions, looking for results that may be better than the aggregation functions commonly applied. The results of such comparisons are promising, when evaluated through image quality metrics.en
dc.description.sponsorshipThe authors Camila Dias and Jessica Bueno thank CAPES for the financial support received. Gracaliz P. Dimuro has funding from CNPq/Brazil (process number 305882/20163). Eduardo N. Borges has funding from FAPERGS (TO 17/2551-0000872-3). Humberto Bustince is supported by the Spanish Ministry of Science and Technology (under project TIN2016-77356-P (AEI/FEDER, UE)).en
dc.format.extent7 p.
dc.format.mimetypeapplication/pdfen
dc.language.isoengen
dc.publisherAtlantis Pressen
dc.relation.ispartofProceedings of the 11th Conference of the European Society for Fuzzy Logic and Technology (EUSFLAT 2019), Vol. 1, pp. 460-466en
dc.rights© 2019, the Authors. This is an open access article under the CC BY-NC license.en
dc.rights.urihttp://creativecommons.org/licenses/by-nc/4.0/
dc.subjectChoquet integralen
dc.subjectAggregation functionsen
dc.subjectImage processingen
dc.titleAggregation functions based on the Choquet integral applied to image resizingen
dc.typeinfo:eu-repo/semantics/conferenceObjecten
dc.typeContribución a congreso / Biltzarrerako ekarpenaes
dc.contributor.departmentUniversidad Pública de Navarra. Departamento de Estadística, Informática y Matemáticases_ES
dc.contributor.departmentUniversidad Pública de Navarra / Nafarroako Unibertsitate Publikoa. ISC - Institute of Smart Citieses_ES
dc.contributor.departmentNafarroako Unibertsitate Publikoa. Estatistika, Informatika eta Matematika Sailaeu
dc.rights.accessRightsinfo:eu-repo/semantics/openAccessen
dc.rights.accessRightsAcceso abierto / Sarbide irekiaes
dc.relation.projectIDinfo:eu-repo/grantAgreement/ES/1PE/TIN2016-77356-Pen
dc.type.versioninfo:eu-repo/semantics/publishedVersionen
dc.type.versionVersión publicada / Argitaratu den bertsioaes


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© 2019, the Authors. This is an open access article under the CC BY-NC license.
Except where otherwise noted, this item's license is described as © 2019, the Authors. This is an open access article under the CC BY-NC license.