Multi Label Learning

Multi-label learning (MLL) is an extension of binary and multi-class classification problems, aiming to assign multiple possible class labels to data instances simultaneously. Each label has a specific semantic association with the data instance. Due to its extensive applications in recommendation systems, image annotation, text classification, and other practical issues, multi-label learning has been a focal area of research.