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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Bibliografische Detailangaben
Hauptverfasser:	Emmanuel de-Graft Johnson Owusu-Ansah, Richard Doamekpor, Richard Kodzo Avuglah, Yaa Kyere Adwubi
Format:	Artigo
Sprache:	Inglês
Veröffentlicht:	Accademia Piceno Aprutina dei Velati 2024-12-01
Schriftenreihe:	Ratio Mathematica
Schlagworte:	credit scoring, smote, oversampling, undersampling, class imbalance, machine learning algorithms
Online-Zugang:	http://eiris.it/ojs/index.php/ratiomathematica/article/view/1601
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Machine Learning Algorithms Analysis of Synthetic Minority Oversampling Technique (SMOTE): Application to Credit Default Prediction

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