Similarity-learning information-fusion schemes for missing data imputation
作者：
Highlights：
• Two novel missing data imputation techniques are proposed.
• These novel imputation techniques handle both numerical and categorical features.
• Experimental comparison based on eleven publicly datasets, two evaluation criteria.
• Missing scores are estimated by learning local and global similarities.
• Top estimations are fused to obtain the final estimation.
摘要
•Two novel missing data imputation techniques are proposed.•These novel imputation techniques handle both numerical and categorical features.•Experimental comparison based on eleven publicly datasets, two evaluation criteria.•Missing scores are estimated by learning local and global similarities.•Top estimations are fused to obtain the final estimation.
论文关键词：Missing data,Imputation,Expectation–Maximization,Similarity learning,Information fusion,Dempster–Shafer theory
论文评审过程：Received 5 January 2019, Revised 14 June 2019, Accepted 15 June 2019, Available online 19 June 2019, Version of Record 18 November 2019.
论文官网地址：https://doi.org/10.1016/j.knosys.2019.06.013
