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AuthorZhang, Haiqing
AuthorLi, Daiwei
AuthorWang, Tao
AuthorLi, Tianrui
AuthorYu, Xi
AuthorBouras, Abdelaziz
Available date2023-04-09T08:34:51Z
Publication Date2018
Publication NameJournal of Intelligent and Fuzzy Systems
ResourceScopus
URIhttp://dx.doi.org/10.3233/JIFS-17415
URIhttp://hdl.handle.net/10576/41767
AbstractAlthough fuzzy rough sets have been considered as a powerful theory to handle real-valued data with uncertainty, fuzzy rough sets based algorithms reached their limit of conveying hesitation information in the processes of making classification decision. Hesitant fuzzy set plays an important role in handling hesitant and uncertainty information. Thus, the fusion of hesitant fuzzy set and fuzzy-rough set is then explored and also applied it into the task of classification. The contributions of this paper include: 1) A dimensionality reduction of hesitant fuzzy sets by investigating the equivalence relation between hesitant fuzzy elements is studied. 2) A new definition of upper and lower approximations of the hesitant fuzzy rough set is given by studying the hesitant fuzzy similarities between hesitant fuzzy elements. 3) A hesitant fuzzy rough sets nearest neighbor (HFRNN) classification algorithm is proposed.The experiments show that the classification algorithm of HFRNN outperforms the existing algorithms of FRNN, VQNN, SNN and ASNN in classification accuracy and execution time. 2018 - IOS Press and the authors. All rights reserved.
SponsorThis research is supported by the National Natural Science Foundation of China (NSFC) (No. 61602064), Science and Technology Agency Project of Sichuan Province (No. 2017HH0088), and Scientific Research Foundation of CUIT (No. KYTZ201615). None of the authors have any competing interests.
Languageen
PublisherIOS Press
Subjectequivalence relations
Hesitant fuzzy rough nearest neighbor
hesitant fuzzy-rough set
similarity measure
TitleHesitant extension of fuzzy-rough set to address uncertainty in classification
TypeArticle
Pagination2535-2550
Issue Number4
Volume Number34
dc.accessType Abstract Only


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