Imputation techniques for the reconstruction of missing interconnected data from higher Educational Institutions
作者：
Highlights：
• Imputation methodology for educational data using information reconstruction and donors.
• Designed to satisfactory impute the difficult case of interconnected time series.
• Application to an important real case: European Tertiary Education Register (ETER).
• Uses a formal and data-driven approach, hence applicable in other contexts.
• Analysis of the imputation accuracy by using artificial missing data.
摘要
•Imputation methodology for educational data using information reconstruction and donors.•Designed to satisfactory impute the difficult case of interconnected time series.•Application to an important real case: European Tertiary Education Register (ETER).•Uses a formal and data-driven approach, hence applicable in other contexts.•Analysis of the imputation accuracy by using artificial missing data.
论文关键词：Data imputation,Information reconstruction,Machine learning,Educational Institutions
论文评审过程：Received 19 June 2020, Revised 10 September 2020, Accepted 6 October 2020, Available online 28 October 2020, Version of Record 24 December 2020.
论文官网地址：https://doi.org/10.1016/j.knosys.2020.106512
