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AuthorWang, Jie
AuthorLi, Daiwei
AuthorZhang, Haiqing
AuthorYu, Xi
AuthorSekhari, Aicha
AuthorOuzrout, Yacine
AuthorBouras, Abdelaziz
Available date2023-04-09T08:34:51Z
Publication Date2020
Publication Name2020 IEEE International Conference on Informatics, IoT, and Enabling Technologies, ICIoT 2020
ResourceScopus
URIhttp://dx.doi.org/10.1109/ICIoT48696.2020.9089571
URIhttp://hdl.handle.net/10576/41762
AbstractData missing is a vitally important issue that influences the classification results in medical field. This paper proposes an improved support vector machine (SVM) imputation algorithm by using strategies of pre-imputation, multiple iteration and grid search (IG-SVMI). Based on the experimental performance, nine UCI datasets and two real datasets are used to compare the proposed algorithm with four existing imputation algorithms (RFI, KNNI, CCMVI and orthogonal coding SVMI). The datasets are considered into two types of originally containing missing value and randomly auto-generating missing of complete dataset. Classification accuracy and NRMSE are used as parameters to judge the efficient of the proposed IG-SVMI algorithm. The experiments have shown that the proposed IG-SVMI algorithm can achieve better results than the benchmark approaches. 2020 IEEE.
SponsorThis research is supported by the National Natural Science Foundation of China (NSFC) (No. 61602064), and Sichuan Province Science and Technology Program, China (No. 2018JY0273, No. 2017HH0088) , European
Languageen
PublisherInstitute of Electrical and Electronics Engineers Inc.
Subjectclassification
data preprocessing
missing values imputation
Support Vector Machine
TitleAn Improvement of Support Vector Machine Imputation Algorithm Based on Multiple Iteration and Grid Search Strategies
TypeConference Paper
Pagination538-543
dc.accessType Abstract Only


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