Yanping Zhu

Heterogeneous stacking framework with adaptive Borderline-SMOTE for imbalanced binary classification: a multi-institutional validation study

Binary classification in imbalanced tabular datasets remains a significant challenge in machine learning, as conventional risk-stratification models exhibit limited discriminative performance and fail to capture nonlinear interactions among heterogeneous features. Existing approaches often suffer from three critical li...