综述机器学习在缺失值填补中的应用，梳理最新进展与研究空白。
Machine Learning for Missing Value Imputation
- 采用系统综述方法，分析2014至2023年超100篇相关论文。
- 总结主流方法表现，指出当前技术在不同数据场景下的优劣。
- 适合数据预处理研究人员，指引未来填补方向。
近年来，大量研究致力于解决缺失值填补（Missing Value Imputation, MVI）问题。MVI旨在为存在一个或多个缺失属性值的数据集提供有效解决方案。人工智能（AI）的进步推动了机器学习（ML）算法和方法的持续创新，使高效填补缺失值成为可能。本文旨在对当前机器学习在MVI中的应用进行系统性、严谨的综述与分析，以增强研究者对该领域的理解，并促进数据预处理中稳健且具有影响力的干预措施的发展。综述遵循系统性文献回顾与元分析报告规范（PRISMA）。共评阅超过100篇2014至2023年间发表的论文，涵盖其方法与成果。此外，对最新文献的考察揭示了MVI方法及其评估趋势。现有研究的成就与局限被深入讨论。最后，文章识别出当前研究中的关键空白，并提出未来研究方向及相关领域的发展趋势。
原文摘要 · Abstract (English)
In recent times, a considerable number of research studies have been carried out to address the issue of Missing Value Imputation (MVI). MVI aims to provide a primary solution for datasets that have one or more missing attribute values. The advancements in Artificial Intelligence (AI) drive the development of new and improved machine learning (ML) algorithms and methods. The advancements in ML have opened up significant opportunities for effectively imputing these missing values. The main objective of this article is to conduct a comprehensive and rigorous review, as well as analysis, of the state-of-the-art ML applications in MVI methods. This analysis seeks to enhance researchers' understanding of the subject and facilitate the development of robust and impactful interventions in data preprocessing for Data Analytics. The review is performed following the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) technique. More than 100 articles published between 2014 and 2023 are critically reviewed, considering the methods and findings. Furthermore, the latest literature is examined to scrutinize the trends in MVI methods and their evaluation. The accomplishments and limitations of the existing literature are discussed in detail. The survey concludes by identifying the current gaps in research and providing suggestions for future research directions and emerging trends in related fields of interest.
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