Hierarchical Classification

Hierarchical classification organizes data into a tree-like structure, aiming to improve classification accuracy and interpretability by leveraging relationships between classes. Current research focuses on developing novel model architectures, such as hierarchical neural networks incorporating attention mechanisms and transformer-based models, and exploring various hierarchy exploitation schemes to optimize performance across diverse datasets and tasks, including those with imbalanced data or missing sensitive attributes. This approach has significant implications for various fields, improving the accuracy and explainability of classification in applications ranging from image recognition and environmental sound classification to financial transaction analysis and medical diagnosis.