Cascade Ensemble

Cascade ensembles are a class of machine learning models that sequentially combine multiple base models, typically progressing from simpler, faster models to more complex, accurate ones. Research focuses on optimizing this cascading process for improved efficiency, particularly through adaptive inference schemes that route data based on model confidence, and enhancing robustness, for example, by employing diversity-based pruning techniques to select the most effective sub-ensembles. These advancements are significant for reducing computational costs in applications like edge computing and improving the reliability of predictions in sensitive domains such as medical image analysis and suicide risk assessment.