Systematic review of using machine learning in imputing missing values

• Main Authors: Alabadla, Mustafa, Fatimah, Sidi, Iskandar, Ishak, Hamidah D., Ibrahim, Lilly Suriani, Affendey, Zafienas, Che Ani, A. Jabar, Marzanah Ab, Bukar, Umar Ali, Devaraj, Navin Kumar, Ahmad Sobri, Muda, Tharek, Anas, Noritah, Omar, Mohd Izham, Mohd Jaya
• Format: Article
• Language: English; English
• Published: Institute of Electrical and Electronics Engineers Inc. 2022
• Subjects: QA75 Electronic computers. Computer science; QA76 Computer software; T Technology (General); TA Engineering (General). Civil engineering (General)
• Online Access: http://umpir.ump.edu.my/id/eprint/38852/1/Systematic%20Review%20of%20Using%20Machine%20Learning%20in%20Imputing%20Missing%20Values.pdf; http://umpir.ump.edu.my/id/eprint/38852/2/Systematic%20review%20of%20using%20machine%20learning%20in%20imputing%20missing%20values_ABS.pdf; http://umpir.ump.edu.my/id/eprint/38852/; https://doi.org/10.1109/ACCESS.2022.3160841; https://doi.org/10.1109/ACCESS.2022.3160841

Missing data are a universal data quality problem in many domains, leading to misleading analysis and inaccurate decisions. Much research has been done to investigate the different mechanisms of missing data and the proper techniques in handling various data types. In the last decade, machine learni...