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Learning Fuzzy Measures for Aggregation in Fuzzy Rule-Based Models
Departament d’Enginyeria Informàtica i Matemàtiques, Universitat Rovira i Virgili, Tarragona, Spain.
Departament d’Enginyeria Informàtica i Matemàtiques, Universitat Rovira i Virgili, Tarragona, Spain.
Departament d’Enginyeria Informàtica i Matemàtiques, Universitat Rovira i Virgili, Tarragona, Spain.
Ophthalmic Service, University Hospital Sant Joan de Reus, Institut d’Investigació Sanitària Pere Virgili (IISPV), Universitat Rovira i Virgili, Reus, Spain.
Vise andre og tillknytning
2018 (engelsk)Inngår i: Modeling Decisions for Artificial Intelligence: 15th International Conference, MDAI 2018, Mallorca, Spain, October 15–18, 2018, Proceedings / [ed] Vicenç Torra, Yasuo Narukawa, Isabel Aguiló, Manuel González-Hidalgo, Springer, 2018, s. 114-127Konferansepaper, Publicerat paper (Fagfellevurdert)
Abstract [en]

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. 

sted, utgiver, år, opplag, sider
Springer, 2018. s. 114-127
Serie
Lecture Notes in Computer Science, ISSN 0302-9743, E-ISSN 1611-3349 ; 11144
Emneord [en]
Aggregation functions, Choquet integral, Diabetic retinopathy, Fuzzy measures, Fuzzy rule-based systems, Sugeno integral, Artificial intelligence, Decision support systems, Eye protection, Fuzzy rules, Integral equations, Risk perception, Sugeno integrals, Fuzzy inference
HSV kategori
Forskningsprogram
Skövde Artificial Intelligence Lab (SAIL); INF301 Data Science
Identifikatorer
URN: urn:nbn:se:his:diva-16416DOI: 10.1007/978-3-030-00202-2_10Scopus ID: 2-s2.0-85055680053ISBN: 978-3-030-00201-5 (tryckt)ISBN: 978-3-030-00202-2 (digital)OAI: oai:DiVA.org:his-16416DiVA, id: diva2:1264382
Konferanse
International Conference on Modeling Decisions for Artificial Intelligence MDAI 2018, 15 October 2018 through 18 October 2018, Mallorca, Spain
Merknad

Also part of the Lecture Notes in Artificial Intelligence book sub series (LNAI, volume 11144)

Tilgjengelig fra: 2018-11-20 Laget: 2018-11-20 Sist oppdatert: 2019-02-14bibliografisk kontrollert

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