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AuthorZhang, Haiqing
AuthorLi, Daiwei
AuthorWang, Tao
AuthorLi, Tianrui
AuthorYu, Xi
AuthorBouras, Abdelaziz
Available date2023-04-09T08:34:51Z
Publication Date2019
Publication NameComputing in Science and Engineering
ResourceScopus
URIhttp://dx.doi.org/10.1109/MCSE.2018.110150747
URIhttp://hdl.handle.net/10576/41766
AbstractThe fusion of hesitant fuzzy set (HFS) and fuzzy-rough set (FRS) is explored and applied into the task of classification due to its capability of conveying hesitant and uncertainty information. In this paper, on the basis of studying the equivalence relations between hesitant fuzzy elements and HFS operation updating, the target instances are classified by employing the lower and upper approximations in hesitant FRS theory. Extensive performance analysis has been conducted including classification accuracy results, execution time, and the impact of k parameter to evaluate the proposed hesitant fuzzy-rough nearest-neighbor (HFRNN) algorithm. The experimental analysis has shown that the proposed HFRNN algorithm significantly outperforms current leading algorithms in terms of fuzzy-rough nearest-neighbor, vaguely quantified rough sets, similarity nearest-neighbor, and aggregated-similarity nearest-neighbor. 1999-2011 IEEE.
SponsorThis work was supported in part by the National Natural Science Foundation of China under Grant 61602064, in part by Science and Technology Agency Project of Sichuan Province under Grant 2017HH0088, in part by the Fundamental Research Funds for the Central Universities under Grant 2682015QM02, and in part by Scientific Research Foundation of CUIT under Grant KYTZ201615.
Languageen
PublisherIEEE Computer Society
Subjectclassification
equivalence relation
fuzzy-rough sets
hesitant fuzzy rough nearest neighbor
hesitant fuzzy set
TitleUncertainty and Equivalence Relation Analysis for Hesitant Fuzzy-Rough Sets and Their Applications in Classification
TypeArticle
Pagination26-39
Issue Number6
Volume Number21
dc.accessType Abstract Only


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