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An Interval-Valued Divergence for Interval-Valued Fuzzy Sets

Characterizing the degree of similarity or difference between two sets is a very important topic, since it has many applications in different areas, including image processing or decision making. Several studies have been done about the comparison of fuzzy sets and its extensions, in particular for...

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Detalles Bibliográficos
Autores principales: Díaz, Susana, Díaz, Irene, Montes, Susana
Formato: Online Artículo Texto
Lenguaje:English
Publicado: 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7274670/
http://dx.doi.org/10.1007/978-3-030-50143-3_18
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author Díaz, Susana
Díaz, Irene
Montes, Susana
author_facet Díaz, Susana
Díaz, Irene
Montes, Susana
author_sort Díaz, Susana
collection PubMed
description Characterizing the degree of similarity or difference between two sets is a very important topic, since it has many applications in different areas, including image processing or decision making. Several studies have been done about the comparison of fuzzy sets and its extensions, in particular for interval-valued fuzzy sets. However, in most of the cases, the results of the comparison is just a number. In order to avoid this reduction of the information, we have introduced a measure for comparing two interval-valued fuzzy sets such that it is an interval itself, which can be reduced to a number if it is necessary. Thus, a richer class of measures is now considered.
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spelling pubmed-72746702020-06-08 An Interval-Valued Divergence for Interval-Valued Fuzzy Sets Díaz, Susana Díaz, Irene Montes, Susana Information Processing and Management of Uncertainty in Knowledge-Based Systems Article Characterizing the degree of similarity or difference between two sets is a very important topic, since it has many applications in different areas, including image processing or decision making. Several studies have been done about the comparison of fuzzy sets and its extensions, in particular for interval-valued fuzzy sets. However, in most of the cases, the results of the comparison is just a number. In order to avoid this reduction of the information, we have introduced a measure for comparing two interval-valued fuzzy sets such that it is an interval itself, which can be reduced to a number if it is necessary. Thus, a richer class of measures is now considered. 2020-05-15 /pmc/articles/PMC7274670/ http://dx.doi.org/10.1007/978-3-030-50143-3_18 Text en © Springer Nature Switzerland AG 2020 This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.
spellingShingle Article
Díaz, Susana
Díaz, Irene
Montes, Susana
An Interval-Valued Divergence for Interval-Valued Fuzzy Sets
title An Interval-Valued Divergence for Interval-Valued Fuzzy Sets
title_full An Interval-Valued Divergence for Interval-Valued Fuzzy Sets
title_fullStr An Interval-Valued Divergence for Interval-Valued Fuzzy Sets
title_full_unstemmed An Interval-Valued Divergence for Interval-Valued Fuzzy Sets
title_short An Interval-Valued Divergence for Interval-Valued Fuzzy Sets
title_sort interval-valued divergence for interval-valued fuzzy sets
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7274670/
http://dx.doi.org/10.1007/978-3-030-50143-3_18
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