Label Tree

Label trees represent hierarchical relationships between classes in classification tasks, improving efficiency and interpretability by leveraging inherent structure within data. Current research focuses on developing algorithms that effectively utilize this hierarchical information, including transformer-based models and capsule networks, often addressing challenges like imbalanced datasets and minimal supervision. These advancements are impacting diverse fields, such as medical image segmentation and text classification, by enabling more accurate and efficient analysis of complex data with improved performance over traditional flat classification methods. The resulting improvements in model accuracy and computational efficiency have significant implications for various applications.