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المؤلفLi, Daiwei
المؤلفLi, Tianrui
المؤلفZhang, Haiqing
المؤلفBouras, Abdelaziz
المؤلفYu, Xi
المؤلفTang, Dan
تاريخ الإتاحة2023-04-09T08:34:49Z
تاريخ النشر2019
اسم المنشورProceedings of IEEE 14th International Conference on Intelligent Systems and Knowledge Engineering, ISKE 2019
المصدرScopus
معرّف المصادر الموحدhttp://dx.doi.org/10.1109/ISKE47853.2019.9170381
معرّف المصادر الموحدhttp://hdl.handle.net/10576/41742
الملخصMissing 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.
اللغةen
الناشرInstitute of Electrical and Electronics Engineers Inc.
الموضوعclassification
Fitted fuzzy-rough set
fuzzy decision
fuzzy similarity relation
missing value imputation
العنوانA Fitted Fuzzy-rough Method for Missing Data Imputation
النوعConference Paper
الصفحات74-81
dc.accessType Abstract Only


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