Multi Model Ensemble

Multi-model ensembles combine predictions from multiple machine learning models to improve accuracy and robustness in diverse applications. Current research focuses on integrating various model architectures, including convolutional neural networks, vision transformers, and decision trees, often employing late fusion techniques to combine their outputs. This approach is proving valuable across fields like medical image analysis (e.g., cardiac segmentation), environmental monitoring (e.g., wildfire fuel mapping), and affective computing (e.g., facial expression recognition), enhancing the reliability and precision of predictions where single models may fall short. The resulting improvements in prediction accuracy and generalization capability have significant implications for both scientific understanding and practical decision-making.