A stacking ensemble model with SMOTE for improved imbalanced classification on credit data
Nur Alamsyah,
Budiman Budiman,
Titan Parama Yoga
et al.
Abstract:This research is based on a significant problem in credit risk analysis in the banking sector caused by class imbalance. We face the problem of the model's inability to accurately identify risks in the ''Charged Off'' class. As a solution, we propose a stacked ensemble approach that utilizes synthetic minority over-sampling technique (SMOTE) to balance the class distribution. Experiments were conducted by applying SMOTE to the training data before training the credit model using gradient boosting (XGBoost) and… Show more
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