AI RESEARCH
A Systematic Evaluation of Imbalance Handling Methods in Biomedical Binary Classification
arXiv CS.LG
•
ArXi:2605.14147v1 Announce Type: new Objective: The primary goal of this study was to systematically examine the impact of commonly used imbalance handling methods (IHMs) on predictive performance in biomedical binary classification, considering the interplay between model complexity and diverse data modalities. Material and Methods: We evaluated five representative IHMs: random undersampling (RUS), random oversampling (ROS), SMOTE, re-weighting (RW), and direct F1-score optimization (DMO), against a raw.