[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121480-en":3,"doc-seo-121480-105":30,"detail-sidebar-cat-0-en-105":91},{"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},121480,1374391974564,"Clementine","https://ap-avatar.wpscdn.com/avatar/14000253aa45c000a9e?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779874745381141002",8,"Research & Report","Enhancing Missing Data Imputation for Migrants Data - A Neutrosophic Set-Based Machine Learning Approach","This study addresses missing data in migrant datasets through a new framework that integrates machine learning with neutrosophic sets to model uncertainty and ambiguity. Neutrosophic representations are used to better manage the incomplete nature of information common in migration research. The work evaluates KNN, SVM, decision tree, random forest, and Ada Boost with imputation strategies including mean/mode, a model-based tree approach, and random value filling. Results show improved imputation accuracy and enhanced model reliability, supporting neutrosophic set-based machine learning as a general solution for missing-data problems across multiple fields.","Enhancing Missing Data Imputation for Migrants Data: A Neutrosophic Set-Based Machine Learning  \nApproach  \nDoaa A. Abdo 1* , A. A. Salama2 , Alaa A. Abdelmegaly3 , Hanan Khadari Mahdi Mahmoud4  \n1Applied statistics and insurance department, faculty of commerce, Mansoura university, Mansoura, Egypt,  \n[doaaashour@mans.edu.eg](doaaashour@mans.edu.eg)  \n2Dept. of Math and Computer Sci., Faculty of Science, Port Said Univ., [Egyptdrsalama44@gmail.com](Egyptdrsalama44@gmail.com),  \n[ahmed_salama_2000@sci.psu.edu.eg](ahmed_salama_2000@sci.psu.edu.eg).  \n3Higher Institute of Advanced Management Sciences and Computers, Al-Buhayrah, Egypt  \n[bintmasr880@yahoo.com](bintmasr880@yahoo.com)  \n4Depatrment of applied statistics at the Nile Higher Institute of commercial sciences and Computer Technology in Mansoura, [hananhkodary@gmail.com](hananhkodary@gmail.com).  \n* [Correspondence: doaaashour@mans.edu.eg](Correspondence: doaaashour@mans.edu.eg)  \nAbstract: This study tackles the problem of missing data in migrant datasets by introducing a new framework that combines machine learning techniques with neutrosophic sets. These sets, which can represent uncertainty and ambiguity, are well-suited for managing the complex nature of missing information in sensitive fields like migration research. We test the effectiveness of KNN, SVM, decision tree, random forest, and Ada Boost algorithms on a migrant dataset, comparing their results using different imputation methods (mean/mode, model-based imputer (simple tree), and random values) . Our research showed that our proposed approach, which used neutrosophic sets, improved imputation accuracy and strengthened model reliability. Our results underscored the potential of neutrosophic set-based machine learning for addressing missing data issues across various fields.  \nKeywords: Missing data imputation, neutrosophic sets, machine learning, migrant data, KNN, SVM, decision tree, random forest, Ada Boost, classification, accuracy, precision, recall, F1-score.  \n1. Introduction  \nIn statistics, missing data, also known as missing values, occurs when no data value is saved for a variable inan observation. Incomplete data is a widespread issue in primary care studies, including clinical trials, observational research, and quality improvement projects. It refers to data points that are not available for analysis, such as migrants who drop out. Missing data is prevalent and can have a substantial impact on the conclusions formed from the data. Missing values in research can lead to bias, reduced validity, inaccurate conclusions and the loss of crucial information from study samples. When missing data is not correctly and handled improperly, it might result in statistical bias and hide the underlying relationship between variables. Missing data might lead to loss of knowledge, affecting study efficiency and interpretation. Also, missing data might reduce the validity and trustworthiness of research findings by increasing bias, causing information loss, and reducing statistical power (Suthar and Goswami, 2012; Hourarip et al., 2014; Ayilara et al., 2019) .  \nThe effect of missing values depends on how much data is missing, what type of data is missing, and why it's missing (Zhang, 2015) . The most common methods which dealing with missing data is delectation missing values and using the mean to fill in missing values. This method resulted in biased estimation of parameters and uncertainty, and decreasing statistical power.  \nMachine learning methods can overcome the shortcomings of traditional approaches like mean, median, and regression imputation (Langkamp et al., 2010; Donders et al., 2006) . Assessing these methods' performance requires careful planning and analysis. Factors like algorithm choice and sampling techniques are key. Therefore, selecting the right strategy for handling missing data is crucial, as improper treatment can lead to inaccurate results. Recently, advanced analysis models like neutroso","cbCaijcy0DJNi1Wq","https://ap.wps.com/l/cbCaijcy0DJNi1Wq","pdf",824957,1,24,"English","en",105,"# Introduction\n## Background on missing data in studies\n## Limitations of traditional missing-data handling\n## Motivation for machine learning and neutrosophic models","[{\"question\":\"Why is missing data a serious issue in migrant datasets?\",\"answer\":\"Missing values can introduce bias, reduce validity, weaken conclusions, and cause loss of crucial information, especially when the missingness is not handled properly.\"},{\"question\":\"What traditional imputation methods are discussed, and what are their drawbacks?\",\"answer\":\"The text highlights detection of missing values and mean-based filling as common approaches, noting biased parameter estimation, increased uncertainty, and reduced statistical power.\"},{\"question\":\"How does the proposed neutrosophic set-based machine learning framework improve missing-data imputation?\",\"answer\":\"Neutrosophic sets incorporate uncertainty and indeterminacy directly into learning, improving imputation accuracy and strengthening model reliability compared with conventional strategies.\"}]","Enhancing Missing Data Imputation for Migrants Data - A Neutrosophic Set-Based Machine Learning Approach | PDF",1785735844,60,{"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":86,"head_meta":88,"extra_data":90,"updated_unix":28},"enhancing-missing-data-imputation-for-migrants-data-a-neutrosophic-set-based-machine-learning-approach","",{"@graph":36,"@context":85},[37,54,68],{"@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/enhancing-missing-data-imputation-for-migrants-data-a-neutrosophic-set-based-machine-learning-approach/121480/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-03",true,{"@type":65,"interactionType":66,"userInteractionCount":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"Why is missing data a serious issue in migrant datasets?","Question",{"text":75,"@type":76},"Missing values can introduce bias, reduce validity, weaken conclusions, and cause loss of crucial information, especially when the missingness is not handled properly.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What traditional imputation methods are discussed, and what are their drawbacks?",{"text":80,"@type":76},"The text highlights detection of missing values and mean-based filling as common approaches, noting biased parameter estimation, increased uncertainty, and reduced statistical power.",{"name":82,"@type":73,"acceptedAnswer":83},"How does the proposed neutrosophic set-based machine learning framework improve missing-data imputation?",{"text":84,"@type":76},"Neutrosophic sets incorporate uncertainty and indeterminacy directly into learning, improving imputation accuracy and strengthening model reliability compared with conventional strategies.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,109,114,119,122,127,130,134],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":29,"slug":108},5,"Comic","comic",{"id":110,"doc_module":4,"doc_module_name":46,"category_name":111,"show_sort_weight":112,"slug":113},6,"Technology",50,"technology",{"id":115,"doc_module":4,"doc_module_name":46,"category_name":116,"show_sort_weight":117,"slug":118},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":120,"slug":121},30,"research-report",{"id":123,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":125,"slug":126},9,"Religion & Spirituality",20,"religion-spirituality",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":128,"show_sort_weight":125,"slug":129},"World Cup","world-cup",{"id":131,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":131,"slug":133},10,"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},19,"General","general"]