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AuthorLi, Daiwei
AuthorLi, Tianrui
AuthorZhang, Haiqing
AuthorBouras, Abdelaziz
AuthorYu, Xi
AuthorTang, Dan
Available date2023-04-09T08:34:49Z
Publication Date2019
Publication NameProceedings of IEEE 14th International Conference on Intelligent Systems and Knowledge Engineering, ISKE 2019
ResourceScopus
URIhttp://dx.doi.org/10.1109/ISKE47853.2019.9170381
URIhttp://hdl.handle.net/10576/41742
AbstractMissing data imputation is a fundamental task for reducing uncertainty and vagueness in medical dataset. Fuzzyrough set has taken very important role to accurate representation original information. This paper proposes Fitted fuzzy-rough imputation algorithms called Fitted FRNNI and Fitted VQNNI by introducing weight coefficients to balance fuzzy similarly relations among training and testing instances. Meanwhile, modification fuzzy decisions of nearest neighbors based on lower/upper approximations are studied. Performance analysis is conducted including classification accuracy analysis, the impact of k parameter and weight coefficient of \alpha and \beta to evaluate the proposed Fitted FRNNI and VQNNI algorithms. Experimental results on 13 benchmark datasets show that the proposed algorithms outperform current leading algorithms. 2019 IEEE.
Languageen
PublisherInstitute of Electrical and Electronics Engineers Inc.
Subjectclassification
Fitted fuzzy-rough set
fuzzy decision
fuzzy similarity relation
missing value imputation
TitleA Fitted Fuzzy-rough Method for Missing Data Imputation
TypeConference Paper
Pagination74-81
dc.accessType Abstract Only


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