Neutrosophic Sets and Systems
Abstract
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.
Recommended Citation
A. Abdo, Doaa; A. A. Salama; Alaa A. Abdelmegaly; and Hanan Khadari Mahdi Mahmoud. "Enhancing Missing Data Imputation for Migrants Data: A Neutrosophic Set-Based Machine Learning Approach." Neutrosophic Sets and Systems 81, 1 (2025). https://digitalrepository.unm.edu/nss_journal/vol81/iss1/29
