Reduction of the size of L-fuzzy contexts. A tool for differential diagnoses of diseases
Fecha
2019Versión
Acceso abierto / Sarbide irekia
Tipo
Artículo / Artikulua
Versión
Versión aceptada / Onetsi den bertsioa
Identificador del proyecto
ES/1PE/TIN2016-77356-P
Impacto
|
10.1080/03081079.2019.1620740
Resumen
Information extraction from an L-fuzzy context becomes a hard problem when we work with a large set of objects and/or attributes. The goal of this paper is to present two different and complementary techniques to reduce the size of the context. First, using overlap indexes, we will establish rankings among the elements of the context that will allow us to determine those that do not provide relev ...
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Information extraction from an L-fuzzy context becomes a hard problem when we work with a large set of objects and/or attributes. The goal of this paper is to present two different and complementary techniques to reduce the size of the context. First, using overlap indexes, we will establish rankings among the elements of the context that will allow us to determine those that do not provide relevant information and eliminate them. Second, by means of Choquet integrals, we will aggregate some objects or attributes of the context in order to jointly use the provided information. One interesting application of the developed theory consists on helping in the differential diagnoses of diseases that share a large number of symptoms and, therefore, that are difficult of distinguish. [--]
Materias
L-fuzzy context,
L-fuzzy concept,
Choquet integral,
Overlap indexes,
Differential diagnosis
Editor
Taylor & Francis
Publicado en
International Journal of General Systems, 2019, vol. 48, no. 7, 692-712
Departamento
Universidad Pública de Navarra. Departamento de Estadística, Informática y Matemáticas /
Nafarroako Unibertsitate Publikoa. Estatistika, Informatika eta Matematika Saila /
Universidad Pública de Navarra/Nafarroako Unibertsitate Publikoa. Institute of Smart Cities - ISC
Versión del editor
Entidades Financiadoras
This paper is partially supported by the Research Group 'Intelligent Systems and Energy (SI+E)' of the University of the Basque Country – UPV/EHU [grant number GIU 16/54] and by the Research Group 'Artificial Intelligence and Approximate Reasoning' of the Public University of Navarra [grant number TIN2016-77356-P].