Affiliations: [a] School of Mathematics and Computer Science, Northwest University for Nationalities, Lanzhou, Gansu, P. R. China | [b] School of Mathematical Sciences, University of Electronic Science and Technology of China, Chengdu, Sichuan, P.R. China
Correspondence:
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Corresponding author. Haidong Zhang, School of Mathematics and Computer Science, Northwest University for Nationalities, Lanzhou, Gansu, P. R. China. Tel./Fax: +86 093 145 12202; E-mail: lingdianstar@163.com.
Abstract: Interval-valued hesitant fuzzy rough set, defined by Zhang et al. [55], is an extension of hesitant fuzzy rough sets, interval-valued fuzzy rough sets and fuzzy rough sets. For further studying the theories and applications of interval-valued hesitant fuzzy rough sets, in this paper, we mainly investigate the topological structures of interval-valued hesitant fuzzy rough sets. Firstly, the concept of interval-valued hesitant fuzzy topological spaces is introduced by us. Then relationships between interval-valued hesitant fuzzy rough approximation spaces and interval-valued hesitant fuzzy topological spaces are further established. It is proved that the set of all lower approximation sets based on an interval-valued hesitant fuzzy reflexive and transitive approximation space forms an interval-valued hesitant fuzzy topology; and conversely, for an interval-valued hesitant fuzzy rough topological space, there exists an interval-valued hesitant fuzzy reflexive and transitive approximation space such that the topology in the interval-valued hesitant fuzzy rough topological space is just the set of all lower approximation sets in the interval-valued hesitant fuzzy reflexive and transitive approximation space. That is to say, there exists a one-to-one correspondence between the set of all interval-valued hesitant fuzzy reflexive and transitive approximation spaces and the set of all interval-valued hesitant fuzzy rough topological spaces. Finally, a practical application is provided to illustrate the validity of the interval-valued hesitant fuzzy rough set model.
