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المؤلفWang, Jie
المؤلفLi, Daiwei
المؤلفZhang, Haiqing
المؤلفYu, Xi
المؤلفSekhari, Aicha
المؤلفOuzrout, Yacine
المؤلفBouras, Abdelaziz
تاريخ الإتاحة2023-04-09T08:34:51Z
تاريخ النشر2020
اسم المنشور2020 IEEE International Conference on Informatics, IoT, and Enabling Technologies, ICIoT 2020
المصدرScopus
معرّف المصادر الموحدhttp://dx.doi.org/10.1109/ICIoT48696.2020.9089571
معرّف المصادر الموحدhttp://hdl.handle.net/10576/41762
الملخصData 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.
راعي المشروعThis 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
اللغةen
الناشرInstitute of Electrical and Electronics Engineers Inc.
الموضوعclassification
data preprocessing
missing values imputation
Support Vector Machine
العنوانAn Improvement of Support Vector Machine Imputation Algorithm Based on Multiple Iteration and Grid Search Strategies
النوعConference Paper
الصفحات538-543
dc.accessType Abstract Only


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