Ensemble Learning

Ensemble learning combines multiple machine learning models to improve prediction accuracy and robustness beyond the capabilities of individual models. Current research focuses on optimizing ensemble composition and diversity, exploring techniques like diversity-optimized pruning, span-level ensembling, and adaptive model selection to enhance performance while mitigating computational costs, particularly in resource-constrained environments. This approach is proving valuable across diverse applications, from healthcare (e.g., disease diagnosis, medication extraction) and natural language processing (e.g., text classification, question answering) to manufacturing (e.g., defect detection, productivity analysis) and beyond, offering improved accuracy and reliability in various prediction tasks.