Hierarchical Multi Label Classification

Hierarchical multi-label classification (HMC) tackles the problem of assigning multiple, hierarchically related labels to data instances, aiming to improve classification accuracy and reflect the inherent structure within the data. Current research focuses on developing robust models, often employing deep learning architectures like transformers and graph convolutional networks, and addressing challenges such as imbalanced datasets, missing annotations, and the efficient handling of complex hierarchical structures. HMC finds applications across diverse fields, including bioinformatics (gene function prediction), image analysis (semantic segmentation), and natural language processing (text classification), enabling more accurate and informative analysis of complex data.