Predicting Bond Defaults in China: A Double-Ensemble Model Leveraging SMOTE for Class Imbalance
This study proposes the Double-Ensemble Learning Classification with SMOTE (DELC-SMOTE), a novel hierarchical framework designed to address the critical challenge of severe class imbalance in financial bond default prediction. The model integrates the Synthetic Minority Over-sampling Technique (SMOT...
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| Hauptverfasser: | , |
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| Format: | Artigo |
| Sprache: | Inglês |
| Veröffentlicht: |
MDPI AG
2026-03-01
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| Schriftenreihe: | Big Data and Cognitive Computing |
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| Online-Zugang: | https://www.mdpi.com/2504-2289/10/3/81 |
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