Ensemble Framework

Ensemble frameworks combine multiple models to improve prediction accuracy, robustness, and interpretability beyond what individual models can achieve. Current research focuses on developing efficient ensemble architectures, including stacked ensembles, voting ensembles, and those leveraging deep learning models like Inception networks and graph convolutional autoencoders, often incorporating techniques like bootstrapping and transfer learning to address data scarcity. These frameworks find applications across diverse fields, from medical diagnostics and time series classification to signal processing and materials science, offering significant improvements in performance and providing valuable insights through explainable AI methods. The resulting advancements enhance the reliability and trustworthiness of machine learning models in various critical applications.