Evolution of L-fuzzy contexts associated with criteria
Fecha
2020Versión
Acceso abierto / Sarbide irekia
Tipo
Contribución a congreso / Biltzarrerako ekarpena
Versión
Versión aceptada / Onetsi den bertsioa
Identificador del proyecto
ES/1PE/TIN2016-77356-P
Impacto
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10.1007/978-3-030-23756-1_8
Resumen
This paper will delve deeper into the general study of the L-fuzzy contexts associated with criteria analyzing situations with a known evolution over time. These criteria may be independent or dependent on each other. We propose two different studies: to build an aggregated context looking for a simplification of the process and to keep the sequence obtaining interesting nuances over time. In bot ...
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This paper will delve deeper into the general study of the L-fuzzy contexts associated with criteria analyzing situations with a known evolution over time. These criteria may be independent or dependent on each other. We propose two different studies: to build an aggregated context looking for a simplification of the process and to keep the sequence obtaining interesting nuances over time. In both cases, aggregation operators will be used in order to address the problem. Finally, a practical example about ratings obtained by different tourist accommodations illustrates the results. [--]
Materias
L-fuzzy concept analysis,
L-fuzzy contexts associated with criteria,
WOWA operators,
Choquet integrals
Editor
Springer
Publicado en
Kahraman C., Cebi S., Cevik Onar S., Oztaysi B., Tolga A., Sari I. (eds): Intelligent and Fuzzy Techniques in Big Data Analytics and Decision Making. INFUS 2019. Advances in Intelligent Systems and Computing, vol 1029. Springer, Cham. 978-3-030-23756-1
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, under Grant GIU16/54 and by the Research Group 'Artificial Intelligence and Approximate Reasoning' of the Public University of Navarre, under TIN2016-77356-P (MINECO, AEI/FEDER, UE)