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Multigranulation Rough Sets in Hesitant Fuzzy Linguistic Information Systems

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Rough Sets (IJCRS 2016)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9920))

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Abstract

Based on lower and upper approximations induced by multiple binary relations, multigranulation rough set theory has become one of the most promising research topics in the domain of rough set theory. Through combining multigranulation rough sets with hesitant fuzzy linguistic term sets, this article introduces a hybrid model of multigranulation rough sets, named a hesitant fuzzy linguistic (HFL) multigranulation rough set. In the framework of granular computing, we first give basic definitions of optimistic and pessimistic hesitant fuzzy linguistic multigranulation rough sets. Then, we explore some important properties about hesitant fuzzy linguistic multigranulation rough sets. Lastly, uncertainty measures for the hesitant fuzzy linguistic multigranulation approximation space are addressed.

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Acknowledgments

The work was supported from the National Natural Science Foundation of China (No. 61272095, 61303107, 61432011, 61573231, U1435212) and the Shanxi Science and Technology Infrastructure (No. 2015091001-0102).

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Authors and Affiliations

  1. Key Laboratory of Computational Intelligence and Chinese Information Processing of Ministry of Education, School of Computer and Information Technology, Shanxi University, Taiyuan, 030006, Shanxi, China

    Chao Zhang, De-Yu Li & Yan-Hui Zhai

Authors
  1. Chao Zhang
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  2. De-Yu Li
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  3. Yan-Hui Zhai
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Corresponding author

Correspondence to De-Yu Li .

Editor information

Editors and Affiliations

  1. Universidad Católica del Norte, Antofagasta, Chile

    Víctor Flores

  2. University of Campinas, Campinas, SP, Brazil

    Fernando Gomide

  3. University of Warsaw, Warsaw, Poland

    Andrzej Janusz

  4. Universidad del Católica del Norte, Antofagasta, Chile

    Claudio Meneses

  5. Tongji University, Shanghai, China

    Duoqian Miao

  6. University of Applied Sciences, Munich, Germany

    Georg Peters

  7. University of Warsaw, Warsaw, Poland

    Dominik Ślęzak

  8. Chongqing University of Posts and Telecommunications, Chongqing, China

    Guoyin Wang

  9. Universidad de Chile, Santiago, Chile

    Richard Weber

  10. University of Regina, Regina, Saskatchewan, Canada

    Yiyu Yao

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Zhang, C., Li, DY., Zhai, YH. (2016). Multigranulation Rough Sets in Hesitant Fuzzy Linguistic Information Systems. In: Flores, V., et al. Rough Sets. IJCRS 2016. Lecture Notes in Computer Science(), vol 9920. Springer, Cham. https://doi.org/10.1007/978-3-319-47160-0_28

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  • DOI: https://doi.org/10.1007/978-3-319-47160-0_28

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-47159-4

  • Online ISBN: 978-3-319-47160-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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