Enhancing Missing Data Imputation for Migrants Data: A Neutrosophic Set-Based Machine Learning  Approach

Doaa A. Abdo; A. A. Salama; Alaa A. Abdelmegaly; Hanan Khadari Mahdi Mahmoud

Authors

Doaa A. Abdo Applied statistics and insurance department, faculty of commerce, Mansoura university, Mansoura, Egypt
A. A. Salama Dept. of Math and Computer Sci., Faculty of Science, Port Said Univ.,
Alaa A. Abdelmegaly Higher Institute of Advanced Management Sciences and Computers, Al-Buhayrah, Egypt
Hanan Khadari Mahdi Mahmoud Depatrment of applied statistics at the Nile Higher Institute of commercial sciences and Computer Technology in Mansoura,

Keywords:

Missing data imputation, neutrosophic sets, machine learning, migrant data, KNN, SVM, decision tree, random forest, Ada Boost, classification, accuracy, precision, recall, F1-score.

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.

DOI: 10.5281/zenodo.14847526