Missing Data Imputation: A Comprehensive Review  

作　　者：Majed Alwateer El-Sayed Atlam Mahmoud Mohammed Abd El-Raouf Osama A. Ghoneim Ibrahim Gad Majed Alwateer;El-Sayed Atlam;Mahmoud Mohammed Abd El-Raouf;Osama A. Ghoneim;Ibrahim Gad(Department of Computer Science, College of Computer Science and Engineering, Taibah University, Yanbu, Saudi Arabia;Computer Science Department, Faculty of Science, Tanta University, Tanta, Egypt;Basic and Applied Science Institute, College of Engineering and Technology, Arab Academy for Science and Technology (AAST), Alexandria, Egypt;Department of Computer Science, Faculty of Computers and informatics, Tanta University, Tanta, Egypt)

机构地区：[1]Department of Computer Science, College of Computer Science and Engineering, Taibah University, Yanbu, Saudi Arabia [2]Computer Science Department, Faculty of Science, Tanta University, Tanta, Egypt [3]Basic and Applied Science Institute, College of Engineering and Technology, Arab Academy for Science and Technology (AAST), Alexandria, Egypt [4]Department of Computer Science, Faculty of Computers and informatics, Tanta University, Tanta, Egypt

出　　处：《Journal of Computer and Communications》2024年第11期53-75,共23页电脑和通信（英文）

摘　　要：Missing data presents a significant challenge in statistical analysis and machine learning, often resulting in biased outcomes and diminished efficiency. This comprehensive review investigates various imputation techniques, categorizing them into three primary approaches: deterministic methods, probabilistic models, and machine learning algorithms. Traditional techniques, including mean or mode imputation, regression imputation, and last observation carried forward, are evaluated alongside more contemporary methods such as multiple imputation, expectation-maximization, and deep learning strategies. The strengths and limitations of each approach are outlined. Key considerations for selecting appropriate methods, based on data characteristics and research objectives, are discussed. The importance of evaluating imputation’s impact on subsequent analyses is emphasized. This synthesis of recent advancements and best practices provides researchers with a robust framework for effectively handling missing data, thereby improving the reliability of empirical findings across diverse disciplines.Missing data presents a significant challenge in statistical analysis and machine learning, often resulting in biased outcomes and diminished efficiency. This comprehensive review investigates various imputation techniques, categorizing them into three primary approaches: deterministic methods, probabilistic models, and machine learning algorithms. Traditional techniques, including mean or mode imputation, regression imputation, and last observation carried forward, are evaluated alongside more contemporary methods such as multiple imputation, expectation-maximization, and deep learning strategies. The strengths and limitations of each approach are outlined. Key considerations for selecting appropriate methods, based on data characteristics and research objectives, are discussed. The importance of evaluating imputation’s impact on subsequent analyses is emphasized. This synthesis of recent advancements and best practices provides researchers with a robust framework for effectively handling missing data, thereby improving the reliability of empirical findings across diverse disciplines.

关 键 词：Missing Data Machine Learning PREDICTION Deep Learning IMPUTATION 

分 类 号：H31[语言文字—英语]