Machine Learning Algorithms Analysis of Synthetic Minority Oversampling Technique (SMOTE): Application to Credit Default Prediction

Machine Learning Algorithms Analysis of Synthetic Minority Oversampling Technique (SMOTE): Application to Credit Default Prediction

Credit default prediction is an important problem in financial risk management. It aims to determine the possibility of borrowers failing on their loan commitments. However, dataset to guide Machine Learning modeling procedure for data driven support suffers from class imbalance. Class imbalance in...

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Bibliographic Details
Main Authors: Emmanuel de-Graft Johnson Owusu-Ansah, Richard Doamekpor, Richard Kodzo Avuglah, Yaa Kyere Adwubi
Format: Article
Language:English
Published: Accademia Piceno Aprutina dei Velati 2024-12-01
Series:Ratio Mathematica
Subjects:
credit scoring, smote, oversampling, undersampling, class imbalance, machine learning algorithms
Online Access:http://eiris.it/ojs/index.php/ratiomathematica/article/view/1601
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