Learning fuzzy measures for aggregation in fuzzy rule-based models
Fecha
2018Autor
Versión
Acceso abierto / Sarbide irekia
Tipo
Contribución a congreso / Biltzarrerako ekarpena
Versión
Versión aceptada / Onetsi den bertsioa
Identificador del proyecto
ES/1PE/TIN2016-77356
Impacto
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10.1007/978-3-030-00202-2_10
Resumen
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 probabil ...
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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. [--]
Materias
Aggregation functions,
Choquet integral,
Diabetic retinopathy,
Fuzzy measures,
Fuzzy rule-based systems,
Sugeno integral
Editor
Springer Verlag
Publicado en
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).
Notas
Comunicación presentada al 15th International Conference on Modeling Decisions for Artificial Intelligence, MDAI 2018 (15 - 18 october 2018).
Departamento
Universidad Pública de Navarra. Departamento de Ingeniería /
Nafarroako Unibertsitate Publikoa. Ingeniaritza Saila
Versión del editor
Entidades Financiadoras
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.