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

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  • Yongming Song
  • Jun Hu

Abstract

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.

Suggested Citation

  • Yongming Song & Jun Hu, 2017. "Vector similarity measures of hesitant fuzzy linguistic term sets and their applications," PLOS ONE, Public Library of Science, vol. 12(12), pages 1-13, December.
    • Handle: RePEc:plo:pone00:0189579
    DOI: 10.1371/journal.pone.0189579
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    References listed on IDEAS

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    Cited by:

    1. Yongming Song, 2018. "Deriving the priority weights from probabilistic linguistic preference relation with unknown probabilities," PLOS ONE, Public Library of Science, vol. 13(12), pages 1-17, December.
    2. Minghua Shi & Yuewen Xiao & Qing Wan, 2019. "Extended Heronian Mean Based on Hesitant Fuzzy Linguistic Information for Multiple Attribute Group Decision-Making," Complexity, Hindawi, vol. 2019, pages 1-19, August.

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