A bi-objective k-nearest-neighbors-based imputation method for multilevel data
作者：
Highlights：
• A new imputation method for multilevel data was proposed.
• The method was described as a bi-objective optimization problem.
• The method was compared with benchmark imputation methods.
• Methods were tested on both simulated and benchmark data.
• The proposed method outperformed the traditional methods in most scenarios.
摘要
•A new imputation method for multilevel data was proposed.•The method was described as a bi-objective optimization problem.•The method was compared with benchmark imputation methods.•Methods were tested on both simulated and benchmark data.•The proposed method outperformed the traditional methods in most scenarios.
论文关键词：Multilevel data,Imputation,k-nearest neighbors
论文评审过程：Received 24 May 2021, Revised 8 February 2022, Accepted 22 April 2022, Available online 16 May 2022, Version of Record 21 May 2022.
论文官网地址：https://doi.org/10.1016/j.eswa.2022.117298
