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Asian Conference on Intelligent Information and Database Systems

ACIIDS 2013: Intelligent Information and Database Systems pp 21–30Cite as

Fuzzy Decision Making Based on Hesitant Fuzzy Linguistic Term Sets

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 7802))

Abstract

This paper presents a new fuzzy decision making method based on likelihood-based comparison relations of hesitant fuzzy linguistic term sets. We also present a similarity measure between hesitant fuzzy linguistic term sets. The proposed method is more simple for fuzzy decision making than the method presented in [18]. It provides us with a useful way for decision making in a fuzzy environment.

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Author information

Authors and Affiliations

  1. Department of Computer and Communication Engineering, De Lin Institute of Technology, New Taipei City, Taiwan, R.O.C.

    Li-Wei Lee

  2. Department of Computer Science and Information Engineering, National Taiwan University of Science and Technology, Taipei, Taiwan, R.O.C.

    Shyi-Ming Chen

Authors
  1. Li-Wei Lee
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  2. Shyi-Ming Chen
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Editor information

Editors and Affiliations

  1. Faculty of Computer Science and Information Systems, Department of Software Engineering, Universiti Teknologi Malaysia, 81310, Johar Baharu, Johor, Malaysia

    Ali Selamat  & Habibollah Haron  & 

  2. Institute of Informatics, Division of Knowledge Managements Systems, Wrocław University of Technology, Str. Wybrzeże Wyspiańskiego 27, 50-370, Wrocław, Poland

    Ngoc Thanh Nguyen

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© 2013 Springer-Verlag Berlin Heidelberg

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Lee, LW., Chen, SM. (2013). Fuzzy Decision Making Based on Hesitant Fuzzy Linguistic Term Sets. In: Selamat, A., Nguyen, N.T., Haron, H. (eds) Intelligent Information and Database Systems. ACIIDS 2013. Lecture Notes in Computer Science(), vol 7802. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-36546-1_3

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  • DOI: https://doi.org/10.1007/978-3-642-36546-1_3

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-36545-4

  • Online ISBN: 978-3-642-36546-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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