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Hesitant fuzzy linguistic rough set over two universes model and its applications

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Abstract

In practical decision making situations, decision makers usually express preferences by evaluating qualitative linguistic alternatives using the hesitant fuzzy linguistic term set. To analyze the hesitant fuzzy linguistic information effectively, we aim to apply the rough set over two universes model. Thus, it is necessary to study the fusion of the hesitant fuzzy linguistic term set and rough set over two universes. This paper proposes a general framework for the study of the hesitant fuzzy linguistic rough set over two universes. First, both the definitions and some fundamental properties will be developed, followed by construction of a general decision making rule based on the hesitant fuzzy linguistic information. Finally, we illustrate the newly proposed approach according to the basis of person-job fit, and discuss its applications compared to classical methods.

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Acknowledgments

The authors would like to thank editor-in-chief and the anonymous reviewers for their valuable comments and suggestions which have significantly improved the quality and presentation of this paper. The work was supported in part from the National Natural Science Foundation of China (No. U1435212, 61272095, 61303107, 61432011, 61573231), Project Supported by National Science and Technology (2012BAH33B01), Shanxi Scholarship Council of China (2013-014) and Shanxi Science and Technology Infrastructure (No. 2015091001-0102).

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Authors and Affiliations

  1. School of Computer and Information Technology, Shanxi University, Taiyuan, 030006, Shanxi, China

    Chao Zhang & Deyu Li

  2. Key Laboratory of Computational Intelligence and Chinese Information Processing of Ministry of Education, Taiyuan, 030006, Shanxi, China

    Deyu Li & Jiye Liang

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  1. Chao Zhang
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  2. Deyu Li
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  3. Jiye Liang
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Correspondence to Deyu Li.

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Zhang, C., Li, D. & Liang, J. Hesitant fuzzy linguistic rough set over two universes model and its applications. Int. J. Mach. Learn. & Cyber. 9, 577–588 (2018). https://doi.org/10.1007/s13042-016-0541-z

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  • DOI: https://doi.org/10.1007/s13042-016-0541-z

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