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Vector similarity measures of hesitant fuzzy linguistic term sets and their applications

In decision making, similarity measure and distance between two objects are crucial to be able to determine the relationship between those objects. Many researchers have received much attention for their research on this subject. In this study, we propose two novel similarity measures between hesita...

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Detalles Bibliográficos
Autores principales: Song, Yongming, Hu, Jun
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5738036/
https://www.ncbi.nlm.nih.gov/pubmed/29261710
http://dx.doi.org/10.1371/journal.pone.0189579
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author Song, Yongming
Hu, Jun
author_facet Song, Yongming
Hu, Jun
author_sort Song, Yongming
collection PubMed
description In decision making, similarity measure and distance between two objects are crucial to be able to determine the relationship between those objects. Many researchers have received much attention for their research on this subject. In this study, we propose two novel similarity measures between hesitant fuzzy linguistic term sets (HFLTSs). In addition, two extensions of Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) are proposed in the hesitant fuzzy linguistic environments. Furthermore, an example of an application concerning traditional Chinese medical diagnosis and an MCDM problem have been given to illustrate the applicability and validation of these similarity measures of HFLTSs. Furthermore, the results of examples demonstrate that the Dice and Jaccard similarity measures are more reasonable than the cosine similarity measure with respect to HFLTSs.
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spelling pubmed-57380362017-12-29 Vector similarity measures of hesitant fuzzy linguistic term sets and their applications Song, Yongming Hu, Jun PLoS One Research Article In decision making, similarity measure and distance between two objects are crucial to be able to determine the relationship between those objects. Many researchers have received much attention for their research on this subject. In this study, we propose two novel similarity measures between hesitant fuzzy linguistic term sets (HFLTSs). In addition, two extensions of Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) are proposed in the hesitant fuzzy linguistic environments. Furthermore, an example of an application concerning traditional Chinese medical diagnosis and an MCDM problem have been given to illustrate the applicability and validation of these similarity measures of HFLTSs. Furthermore, the results of examples demonstrate that the Dice and Jaccard similarity measures are more reasonable than the cosine similarity measure with respect to HFLTSs. Public Library of Science 2017-12-20 /pmc/articles/PMC5738036/ /pubmed/29261710 http://dx.doi.org/10.1371/journal.pone.0189579 Text en © 2017 Song, Hu http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Song, Yongming
Hu, Jun
Vector similarity measures of hesitant fuzzy linguistic term sets and their applications
title Vector similarity measures of hesitant fuzzy linguistic term sets and their applications
title_full Vector similarity measures of hesitant fuzzy linguistic term sets and their applications
title_fullStr Vector similarity measures of hesitant fuzzy linguistic term sets and their applications
title_full_unstemmed Vector similarity measures of hesitant fuzzy linguistic term sets and their applications
title_short Vector similarity measures of hesitant fuzzy linguistic term sets and their applications
title_sort vector similarity measures of hesitant fuzzy linguistic term sets and their applications
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5738036/
https://www.ncbi.nlm.nih.gov/pubmed/29261710
http://dx.doi.org/10.1371/journal.pone.0189579
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