Selective Ensemble

Selective ensembles aim to improve the reliability and consistency of machine learning predictions by strategically combining multiple models. Current research focuses on developing efficient ensemble methods, such as those leveraging low-rank adaptations for deep networks or generative adversarial networks for complex mappings, and on sophisticated selection criteria that balance model diversity and stability, often employing metrics like kappa and F-score. This approach is particularly valuable in high-stakes applications requiring calibrated uncertainty estimates and consistent predictions, enhancing the trustworthiness and interpretability of machine learning models across diverse domains.