A class center based approach for missing value imputation
作者：
Highlights：
• A novel missing value imputation is introduced, which is composed of two modules.
• Each class center and its distances from the other observed data are measured to identify a threshold.
• Then, the identified threshold is used for missing value imputation.
• The proposed approach outperforms the other approaches for both numerical and mixed datasets.
• It requires much less imputation time than the machine learning based methods.
摘要
•A novel missing value imputation is introduced, which is composed of two modules.•Each class center and its distances from the other observed data are measured to identify a threshold.•Then, the identified threshold is used for missing value imputation.•The proposed approach outperforms the other approaches for both numerical and mixed datasets.•It requires much less imputation time than the machine learning based methods.
论文关键词：Data mining,Missing value imputation,Incomplete datasets,Machine learning
论文评审过程：Received 2 August 2017, Revised 10 March 2018, Accepted 19 March 2018, Available online 21 March 2018, Version of Record 11 May 2018.
论文官网地址：https://doi.org/10.1016/j.knosys.2018.03.026
