Enhancing Missing Data Imputation for Migrants Data: A Neutrosophic Set-Based Machine Learning Approach
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 sen...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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University of New Mexico
2025-04-01
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| Series: | Neutrosophic Sets and Systems |
| Subjects: | |
| Online Access: | https://fs.unm.edu/NSS/29MissingData.pdf |
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| author | Doaa A. Abdo A. A. Salama Alaa A. Abdelmegaly Hanan Khadari Mahdi Mahmoud |
| author_facet | Doaa A. Abdo A. A. Salama Alaa A. Abdelmegaly Hanan Khadari Mahdi Mahmoud |
| author_sort | Doaa A. Abdo |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-301fa33a777e4486891775ae6d35677f |
| institution | Kabale University |
| issn | 2331-6055 2331-608X |
| language | English |
| publishDate | 2025-04-01 |
| publisher | University of New Mexico |
| record_format | Article |
| series | Neutrosophic Sets and Systems |
| spelling | doaj-art-301fa33a777e4486891775ae6d35677f2025-08-25T09:37:37ZengUniversity of New MexicoNeutrosophic Sets and Systems2331-60552331-608X2025-04-018147950210.5281/zenodo.14847526Enhancing Missing Data Imputation for Migrants Data: A Neutrosophic Set-Based Machine Learning ApproachDoaa A. AbdoA. A. SalamaAlaa A. AbdelmegalyHanan Khadari Mahdi MahmoudThis 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. https://fs.unm.edu/NSS/29MissingData.pdfmissing data imputationneutrosophic setsmachine learningmigrant dataknnsvmdecision treerandom forestada boostclassificationaccuracyprecisionrecallf1-score |
| spellingShingle | Doaa A. Abdo A. A. Salama Alaa A. Abdelmegaly Hanan Khadari Mahdi Mahmoud Enhancing Missing Data Imputation for Migrants Data: A Neutrosophic Set-Based Machine Learning Approach Neutrosophic Sets and Systems missing data imputation neutrosophic sets machine learning migrant data knn svm decision tree random forest ada boost classification accuracy precision recall f1-score |
| title | Enhancing Missing Data Imputation for Migrants Data: A Neutrosophic Set-Based Machine Learning Approach |
| title_full | Enhancing Missing Data Imputation for Migrants Data: A Neutrosophic Set-Based Machine Learning Approach |
| title_fullStr | Enhancing Missing Data Imputation for Migrants Data: A Neutrosophic Set-Based Machine Learning Approach |
| title_full_unstemmed | Enhancing Missing Data Imputation for Migrants Data: A Neutrosophic Set-Based Machine Learning Approach |
| title_short | Enhancing Missing Data Imputation for Migrants Data: A Neutrosophic Set-Based Machine Learning Approach |
| title_sort | enhancing missing data imputation for migrants data a neutrosophic set based machine learning approach |
| topic | missing data imputation neutrosophic sets machine learning migrant data knn svm decision tree random forest ada boost classification accuracy precision recall f1-score |
| url | https://fs.unm.edu/NSS/29MissingData.pdf |
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