[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119820-en":3,"doc-seo-119820-105":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},119820,8796095360427,"Lucas Martin","https://ap-avatar.wpscdn.com/davatar_994ba38a5ba835b3df7d355c54d3ed8d",8,"Research & Report","Analysis of Machine Learning Based Imputation of Missing Data","Data analysis and classification are strongly influenced by the presence of missing values, since common deletion strategies reduce records and can distort outcomes, while imputation risks introducing incorrect predictions. This study analyzes machine learning-based imputation approaches to generate missing entries more accurately. K-nearest neighbors (KNN) and sequential KNN (SKNN) are compared against list-wise deletion (LD). Using a statistical deletion baseline and ML imputations, results are evaluated with classifiers including support vector machines and decision trees, focusing on overall accuracy.","POLITECNICO DI TORINO Repository ISTITUZIONALE  \nAnalysis of Machine Learning Based Imputation of Missing Data  \nOriginal  \nAnalysis of Machine Learning Based Imputation of Missing Data / Tahir Hussain Rizvia, Syed; Yasir Latif, Muhammad; Saad Amin, Muhammad; Jabeur Telmoudi, Achraf; Shah, NASIR ALI. -In: CYBERNETICS AND SYSTEMS. -ISSN 1087-6553. -ELETTRONICO. -15:(2023) . [10 . 1080/01969722 .2023.2247257]  \nAvailability:  \nThis version is available at: 11583/2979607 since: 2023-09-10T07:41:23Z  \nPublisher:  \nTaylor & Francis  \nPublished  \nDOI:10.1080/01969722.2023.2247257  \nTerms of use:  \nThis article is made available under terms and conditions as specified in the corresponding bibliographic description in the repository  \nPublisher copyright  \n(Article begins on next page)  \n17 October 2023  \nCybernetics and Systems  \nAn International Journal  \nISSN: (Print) (Online) Journal homepage: [https://www.tandfonline.com/loi/ucbs20](https://www.tandfonline.com/loi/ucbs20)  \nAnalysis of Machine Learning Based Imputation of Missing Data  \nSyed Tahir Hussain Rizvi, Muhammad Yasir Latif, Muhammad Saad Amin, Achraf Jabeur Telmoudi & Nasir Ali Shah  \nTo cite this article: Syed Tahir Hussain Rizvi, Muhammad Yasir Latif, Muhammad Saad Amin, Achraf Jabeur Telmoudi & Nasir Ali Shah (2023): Analysis of Machine Learning Based Imputation of Missing Data, Cybernetics and Systems, DOI: 10.1080/01969722.2023.2247257  \nTo link to this article: [https://doi.org/10.1080/01969722.2023.2247257](https://doi.org/10.1080/01969722.2023.2247257)  \n© 2023 The Author(s) . Published with license by Taylor & Francis Group, LLC.  \n\n|  Published online: 09 Sep 2023. |\n| --- |\n|  Submit your article to this journal  |\n|  View related articles  |\n|  View Crossmark data |\n\nFull Terms & Conditions of access and use can be found at [https://www.tandfonline.com/action/journalInformation?journalCode=ucbs20](https://www.tandfonline.com/action/journalInformation?journalCode=ucbs20)  \nCYBERNETICS AND SYSTEMS: AN INTERNATIONAL JOURNAL  \n[https://doi.org/10.1080/01969722.2023.2247257](https://doi.org/10.1080/01969722.2023.2247257)  \nAnalysis of Machine Learning Based Imputation of Missing Data  \nSyed Tahir Hussain Rizvia, Muhammad Yasir Latifb, Muhammad Saad Aminc, Achraf Jabeur Telmoudid, and Nasir Ali Shahe  \naDepartment of Electrical Engineering and Computer Science, University of Stavanger, Stavanger, Norway; bEducative, Inc., Islamabad, Pakistan; cDipartimento di Informatica, Universita Degli Studidi Torino, Torino, Italy; dLISIER Laboratory, The National Higher Engineering School of Tunis (ENSIT), University of Tunis, Tunis, Tunisia; eDipartimento Di Elettronica E Telecomunicazioni, Politecnico di Torino, Torino, Italy  \nABSTRACT  \nData analysis and classification can be affected by the availability of missing data in datasets. To deal with missing data, either deletion- or imputation-based methods are used that result in the reduction of data records or imputation of incorrect predicted value. Quality of imputed data can be significantly improved if missing values are generated accurately using machine learning algorithms. In this work, an analysis of machine learning-based algorithms for missing data imputation is performed. The K-nearest neighbors (KNN) and Sequential KNN (SKNN) algorithms are used to impute missing values in datasets using machine learning. Missing values handled using a statistical deletion approach (List-wise Deletion (LD)) and ML-based imputation methods (KNN and SKNN) are then tested and compared using different ML classifiers (Support Vector Machine and Decision Tree) to evaluate the effectiveness of imputed data. The used algorithms are compared in terms of accuracy, and results yielded that the ML-based imputation method (SKNN) outperforms the LDbased approach and KNN method in terms of the effectiveness of handling missing data in almost every dataset with both classification algorithms (SVM and DT).  \nKEYWORDS  \nImputation; imputation us","cbCaif5ljmycFXux","https://ap.wps.com/l/cbCaif5ljmycFXux","pdf",2184764,1,17,"English","en",105,"# Abstract\n# Introduction\n## Impact of missing data on real-world datasets\n## Sources of missing values","[{\"question\":\"Why does missing data affect analysis and classification results?\",\"answer\":\"Missing values can make datasets incomplete and reduce the accuracy of downstream analysis and classification. Deletion-based methods shrink the data, while imputation may introduce incorrect predicted values.\"},{\"question\":\"Which imputation methods are compared in the study?\",\"answer\":\"The study compares list-wise deletion (LD) with machine learning-based imputation methods, specifically K-nearest neighbors (KNN) and Sequential KNN (SKNN).\"},{\"question\":\"How are the imputed datasets evaluated?\",\"answer\":\"Imputed results are tested and compared using different machine learning classifiers, including Support Vector Machine (SVM) and Decision Tree (DT), with effectiveness measured mainly through accuracy.\"}]","Analysis of Machine Learning Based Imputation of Missing Data | PDF",1785726486,43,{"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":86,"head_meta":88,"extra_data":90,"updated_unix":28},"analysis-of-machine-learning-based-imputation-of-missing-data","",{"@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/analysis-of-machine-learning-based-imputation-of-missing-data/119820/",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 does missing data affect analysis and classification results?","Question",{"text":75,"@type":76},"Missing values can make datasets incomplete and reduce the accuracy of downstream analysis and classification. Deletion-based methods shrink the data, while imputation may introduce incorrect predicted values.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which imputation methods are compared in the study?",{"text":80,"@type":76},"The study compares list-wise deletion (LD) with machine learning-based imputation methods, specifically K-nearest neighbors (KNN) and Sequential KNN (SKNN).",{"name":82,"@type":73,"acceptedAnswer":83},"How are the imputed datasets evaluated?",{"text":84,"@type":76},"Imputed results are tested and compared using different machine learning classifiers, including Support Vector Machine (SVM) and Decision Tree (DT), with effectiveness measured mainly through accuracy.","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},[93,97,101,105,110,115,120,123,128,131,135],{"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":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]