Hierarchical Ensemble

Hierarchical ensembles combine multiple prediction models, often of diverse architectures (e.g., transformers and traditional methods), arranged in a layered structure to improve overall accuracy and robustness. Current research focuses on optimizing ensemble construction algorithms, such as Minimum Bayes Risk decoding, and applying this approach to various tasks including sentiment analysis, text summarization, and fine-grained image classification. This technique demonstrates significant performance gains across diverse domains, particularly in scenarios with limited data or complex label hierarchies, offering a powerful tool for enhancing the reliability and accuracy of machine learning models in practical applications.