Learning fuzzy measures for aggregation in fuzzy rule-based models

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dc.contributor.authorSaleh, Emran
dc.contributor.authorValls, Aida
dc.contributor.authorMoreno Pérez, Antonio
dc.contributor.authorRomero-Aroca, Pedro
dc.contributor.authorTorra, Vicenç
dc.contributor.authorBustince Sola, Humberto
dc.contributor.departmentIngenieríaes_ES
dc.contributor.departmentIngeniaritzaeu
dc.date.accessioned2019-06-24T11:13:20Z
dc.date.available2020-09-16T23:00:11Z
dc.date.issued2018
dc.descriptionComunicación presentada al 15th International Conference on Modeling Decisions for Artificial Intelligence, MDAI 2018 (15 - 18 october 2018).en
dc.description.abstractFuzzy measures are used to express background knowledge of the information sources. In fuzzy rule-based models, the rule confidence gives an important information about the final classes and their relevance. This work proposes to use fuzzy measures and integrals to combine rules confidences when making a decision. A Sugeno $$\lambda $$ -measure and a distorted probability have been used in this process. A clinical decision support system (CDSS) has been built by applying this approach to a medical dataset. Then we use our system to estimate the risk of developing diabetic retinopathy. We show performance results comparing our system with others in the literature.en
dc.description.sponsorshipThis work is supported by the URV grant 2017PFR-URV-B2-60, and by the Spanish research projects no: PI12/01535 and PI15/01150 for (Instituto de Salud Carlos III and FEDER funds). Mr. Saleh has a Pre-doctoral grant (FI 2017) provided by the Catalan government and an Erasmus+ travel grant by URV. Prof. Bustince acknowledges the support of Spanish project TIN2016-77356-P.en
dc.embargo.lift2020-09-16
dc.embargo.terms2020-09-16
dc.format.extent13 p.
dc.format.mimetypeapplication/pdfen
dc.identifier.doi10.1007/978-3-030-00202-2_10
dc.identifier.urihttps://academica-e.unavarra.es/handle/2454/33478
dc.language.isoengen
dc.publisherSpringer Verlagen
dc.relation.ispartofSaleh E., Valls A., Moreno A., Romero-Aroca P., Torra V., Bustince H. (2018) Learning Fuzzy Measures for Aggregation in Fuzzy Rule-Based Models. In: Torra V., Narukawa Y., Aguiló I., González-Hidalgo M. (eds) Modeling Decisions for Artificial Intelligence. MDAI 2018. Lecture Notes in Computer Science, vol 11144. Springer, Cham. ISBN 978-3-030-00201-5- ISBN 978-3-030-00202-2 (eBook).en
dc.relation.projectIDinfo:eu-repo/grantAgreement/AEI/ Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016/TIN2016-77356-P
dc.relation.projectIDinfo:eu-repo/grantAgreement/MINECO//PI12%2F01535/ES/
dc.relation.projectIDinfo:eu-repo/grantAgreement/MINECO//PI15%2F01150/ES/
dc.relation.publisherversionhttps://doi.org/10.1007/978-3-030-00202-2_10
dc.rights© Springer Nature Switzerland AG 2018, corrected publication 2018en
dc.rights.accessRightsinfo:eu-repo/semantics/openAccess
dc.subjectAggregation functionsen
dc.subjectChoquet integralen
dc.subjectDiabetic retinopathyen
dc.subjectFuzzy measuresen
dc.subjectFuzzy rule-based systemsen
dc.subjectSugeno integralen
dc.titleLearning fuzzy measures for aggregation in fuzzy rule-based modelsen
dc.typeinfo:eu-repo/semantics/conferenceObject
dc.type.versioninfo:eu-repo/semantics/acceptedVersion
dspace.entity.typePublication
relation.isAuthorOfPublicationd2989604-f204-42e4-9b1f-0a330e6443b1
relation.isAuthorOfPublication1bdd7a0e-704f-48e5-8d27-4486444f82c9
relation.isAuthorOfPublication.latestForDiscoveryd2989604-f204-42e4-9b1f-0a330e6443b1

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