[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-122536-en":3,"doc-seo-122536-105":30,"detail-sidebar-cat-0-en-105":92},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},122536,1099514068365,"Aurelia","https://ap-avatar.wpscdn.com/avatar/10000253d8d9f28188e?_k=1776742907772140068",8,"Research & Report","Machine Learning for Missing Value Imputation - A Comprehensive Review","Machine Learning for Missing Value Imputation reviews state-of-the-art ML approaches for Missing Value Imputation (MVI), targeting datasets with one or more missing attribute values. Building on AI and ML advances, the work surveys over 100 research articles from 2014 to 2023 using PRISMA-guided systematic review methods. The analysis compares methods and findings, examines trends and evaluation practices, and discusses the strengths and limitations of existing literature. It concludes by highlighting research gaps and proposing future directions and emerging trends for robust data preprocessing in analytics.","Machine Learning for Missing Value Imputation  \nAbu Fuad Ahmad, Khaznah Alshammari, Istiaque Ahmed, and MD Shohel Sayed  \nAbstract—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.  \nImpact Statement — This is a comprehensive review that explores the application of machine learning techniques in handling missing data across various domains. The study examines existing literature and methodologies, highlighting the significance of ML-based approaches in addressing the challenge of missing values in datasets. The paper elucidates the strengthsand limitations of different ML algorithms and their effectiveness in imputing missing values across different types of data. Additionally, the research identifies emerging trends, challenges, and future research directions in the field of missing value imputation using ML. The findings of this research contribute to advancing the understanding of ML techniques for handling missing data and provide valuable insights for practitioners and researchers seeking to develop more robust and accurate imputation methods in data analysis and machine learning applications.  \nThis paragraph of the first footnote will contain the date on which you submitted your paper for review. It will also contain support information, including sponsor and financial support acknowledgment.  \nAbu Fuad Ahmad is with the department of Computer Science of New Mexico State University, Las Cruces, NM 88001 USA (e-mail: [fuad@nmsu.edu](fuad@nmsu.edu)).  \nKhaznah Alshammari is with the department of Computer Science of New Mexico State University, Las Cruces, NM 88001 USA, on leave from Northern Border University, Rafha, Saudi Arabia ([e-mail: Kalshamm@nmsu.edu](e-mail: Kalshamm@nmsu.edu)).  \nIndex Terms—Artificial Intelligence, Machine Learning, Missing Value, Imputation, Review.  \nI. INTRODUCTION  \nARTIFICIAL Intelligence (AI) and Machine Learning (ML)  \nare two interconnected fields. AI encompasses the development of intelligent systems that can perform tasks that typically require human intelligence. ML is a key component of many AI systems as it provides algorithms and methods for learning from data. As ML techniques improve and evolve, they enable AI systems to become more capable, efficient, and accurate in performing a wide range of tasks. ML techniques serve as the foundation for many AI applications and advancements in AI drive the development of new and improved ML algorithms and methods.  \nThe term \"machine learning\" is commonly used in various fields, but there is no universally agreed-upon definition. This lack of consensus can be attributed t","cbCaiuc69jFRUO9n","https://ap.wps.com/l/cbCaiuc69jFRUO9n","pdf",810613,1,17,"English","en",105,"# Introduction\n## Artificial Intelligence and Machine Learning\n## Definitions and Background of Machine Learning","[{\"question\":\"What problem does missing value imputation (MVI) address?\",\"answer\":\"MVI addresses datasets that contain one or more missing attribute values, enabling usable analyses and learning despite incomplete data.\"},{\"question\":\"How is the review conducted in this article?\",\"answer\":\"The review follows the PRISMA technique, critically examining more than 100 articles published between 2014 and 2023.\"},{\"question\":\"What does the paper provide at the end of the survey?\",\"answer\":\"It discusses accomplishments and limitations, identifies research gaps, and suggests future research directions and emerging trends in ML-based MVI.\"}]","Machine Learning for Missing Value Imputation - A Comprehensive Review | PDF",1785811141,43,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":87,"head_meta":89,"extra_data":91,"updated_unix":28},"machine-learning-for-missing-value-imputation-a-comprehensive-review","",{"@graph":36,"@context":86},[37,54,69],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/machine-learning-for-missing-value-imputation-a-comprehensive-review/122536/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-05","2026-08-04",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"What problem does missing value imputation (MVI) address?","Question",{"text":76,"@type":77},"MVI addresses datasets that contain one or more missing attribute values, enabling usable analyses and learning despite incomplete data.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"How is the review conducted in this article?",{"text":81,"@type":77},"The review follows the PRISMA technique, critically examining more than 100 articles published between 2014 and 2023.",{"name":83,"@type":74,"acceptedAnswer":84},"What does the paper provide at the end of the survey?",{"text":85,"@type":77},"It discusses accomplishments and limitations, identifies research gaps, and suggests future research directions and emerging trends in ML-based MVI.","https://schema.org",{"og:url":52,"og:type":88,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":90,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":93},[94,98,102,106,111,116,121,124,129,132,136],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":95,"show_sort_weight":96,"slug":97},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":99,"show_sort_weight":100,"slug":101},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":103,"show_sort_weight":104,"slug":105},"Exam",70,"exam",{"id":107,"doc_module":4,"doc_module_name":46,"category_name":108,"show_sort_weight":109,"slug":110},5,"Comic",60,"comic",{"id":112,"doc_module":4,"doc_module_name":46,"category_name":113,"show_sort_weight":114,"slug":115},6,"Technology",50,"technology",{"id":117,"doc_module":4,"doc_module_name":46,"category_name":118,"show_sort_weight":119,"slug":120},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":122,"slug":123},30,"research-report",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":126,"show_sort_weight":127,"slug":128},9,"Religion & Spirituality",20,"religion-spirituality",{"id":127,"doc_module":4,"doc_module_name":46,"category_name":130,"show_sort_weight":127,"slug":131},"World Cup","world-cup",{"id":133,"doc_module":4,"doc_module_name":46,"category_name":134,"show_sort_weight":133,"slug":135},10,"Lifestyle","lifestyle",{"id":137,"doc_module":4,"doc_module_name":46,"category_name":138,"show_sort_weight":107,"slug":139},19,"General","general"]