Weighted Ensemble

Weighted ensemble methods combine predictions from multiple machine learning models, aiming to improve overall accuracy, robustness, and generalization performance beyond that of individual models. Current research focuses on optimizing ensemble weights using techniques like information-theoretic approaches, quadratic programming, and adaptive weighting schemes, often applied to diverse model architectures including neural networks (e.g., CNNs, GNNs) and ensemble methods like Random Forests and Gradient Boosting. These advancements are impacting various fields, from medical image analysis and brain tumor detection to time series forecasting and energy consumption prediction, by providing more accurate and reliable predictions in challenging scenarios.