Novel Ensemble

Novel ensemble methods are increasingly used to improve the performance and robustness of machine learning models across diverse applications. Current research focuses on developing ensemble architectures tailored to specific challenges, such as data scarcity in medical imaging (using transfer and self-supervised learning), non-identically distributed data in federated learning (through collaborative ensemble construction), and handling concept drift in data streams (via dynamic ensemble diversification). These advancements enhance model accuracy, reliability, and explainability, leading to significant improvements in various fields, including medical diagnostics, industrial automation, and time series forecasting.