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

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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Bibliographic Details
Main Authors: Doaa A. Abdo, A. A. Salama, Alaa A. Abdelmegaly, Hanan Khadari Mahdi Mahmoud
Format: Article
Language:English
Published: University of New Mexico 2025-04-01
Series:Neutrosophic Sets and Systems
Subjects:
missing data imputation
neutrosophic sets
machine learning
migrant data
knn
svm
decision tree
random forest
ada boost
classification
accuracy
precision
recall
f1-score
Online Access:https://fs.unm.edu/NSS/29MissingData.pdf
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https://fs.unm.edu/NSS/29MissingData.pdf

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