Reduction of the size of L-fuzzy contexts. A tool for differential diagnoses of diseases

Date

2019

Authors

Alcalde, Cristina

Director

Publisher

Taylor & Francis
Acceso abierto / Sarbide irekia
Artículo / Artikulua
Versión aceptada / Onetsi den bertsioa

Project identifier

  • ES/1PE/TIN2016-77356-P/
Impacto
No disponible en Scopus

Abstract

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.

Description

Keywords

L-fuzzy context, L-fuzzy concept, Choquet integral, Overlap indexes, Differential diagnosis

Department

Estatistika, Informatika eta Matematika / Institute of Smart Cities - ISC / Estadística, Informática y Matemáticas

Faculty/School

Degree

Doctorate program

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