Model Ensemble

Model ensembles, combining predictions from multiple individual models, aim to improve prediction accuracy, robustness, and uncertainty quantification in diverse machine learning applications. Current research focuses on optimizing ensemble strategies, including exploring diverse model architectures (e.g., U-Nets, Swin Transformers, YOLOv8) and employing techniques like Bayesian optimization and active learning to enhance performance and efficiency. This approach has shown significant impact across various fields, from medical image analysis (e.g., brain tumor segmentation, retinal vascular analysis) and climate modeling to natural language processing and agricultural pattern recognition, demonstrating improved results compared to single-model approaches.