Adaptive Ensemble

Adaptive ensemble methods aim to improve the performance and robustness of machine learning models by intelligently combining multiple base learners. Current research focuses on developing adaptive weighting schemes and dynamic model selection strategies, often incorporating diverse base models such as XGBoost, LightGBM, neural networks, and rule-based systems, within frameworks like stacking ensembles. This approach addresses limitations of single models, particularly in handling noisy data, out-of-distribution samples, and concept drift, leading to improved accuracy and generalization across various applications including recommendation systems, churn prediction, and autonomous driving.