Ensemble Algorithm

Ensemble algorithms combine multiple individual machine learning models to improve predictive accuracy and robustness. Current research focuses on optimizing ensemble size and diversity, developing novel aggregation methods (like margin-maximizing and Bayesian approaches), and addressing challenges such as prediction instability and interpretability through techniques like rule extraction and post-hoc regularization. These advancements are impacting diverse fields, from medical image analysis (e.g., stroke lesion segmentation) to resource-constrained applications and improving the efficiency and explainability of machine learning models.