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Learning fuzzy measures for aggregation in fuzzy rule-based models
dc.creator | Saleh, Emran | es_ES |
dc.creator | Valls, Aida | es_ES |
dc.creator | Moreno, Antonio | es_ES |
dc.creator | Romero-Aroca, Pedro | es_ES |
dc.creator | Torra, Vicenç | es_ES |
dc.creator | Bustince Sola, Humberto | es_ES |
dc.date.accessioned | 2019-06-24T11:13:20Z | |
dc.date.available | 2020-09-16T23:00:11Z | |
dc.date.issued | 2018 | |
dc.identifier.uri | https://hdl.handle.net/2454/33478 | |
dc.description | Comunicación presentada al 15th International Conference on Modeling Decisions for Artificial Intelligence, MDAI 2018 (15 - 18 october 2018). | en |
dc.description.abstract | Fuzzy 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.sponsorship | This 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.format.extent | 13 p. | |
dc.format.mimetype | application/pdf | en |
dc.language.iso | eng | en |
dc.publisher | Springer Verlag | en |
dc.relation.ispartof | Saleh 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.rights | © Springer Nature Switzerland AG 2018, corrected publication 2018 | en |
dc.subject | Aggregation functions | en |
dc.subject | Choquet integral | en |
dc.subject | Diabetic retinopathy | en |
dc.subject | Fuzzy measures | en |
dc.subject | Fuzzy rule-based systems | en |
dc.subject | Sugeno integral | en |
dc.title | Learning fuzzy measures for aggregation in fuzzy rule-based models | en |
dc.type | info:eu-repo/semantics/conferenceObject | en |
dc.type | Contribución a congreso / Biltzarrerako ekarpena | es |
dc.contributor.department | Ingeniería | es_ES |
dc.contributor.department | Ingeniaritza | eu |
dc.rights.accessRights | info:eu-repo/semantics/openAccess | en |
dc.rights.accessRights | Acceso abierto / Sarbide irekia | es |
dc.embargo.terms | 2020-09-16 | |
dc.identifier.doi | 10.1007/978-3-030-00202-2_10 | |
dc.relation.projectID | info:eu-repo/grantAgreement/ES/1PE/TIN2016-77356 | en |
dc.relation.publisherversion | https://doi.org/10.1007/978-3-030-00202-2_10 | |
dc.type.version | info:eu-repo/semantics/acceptedVersion | en |
dc.type.version | Versión aceptada / Onetsi den bertsioa | es |