Multi-label classification

Multi-label classification is a concept in mathematics and machine learning. Traditional single-label classification is concerned with learning from a set of examples that are associated with a single label $$l$$ from a set of disjoint labels $$L, |L| > 1 $$. In multi-label classification, the examples are associated with a set of labels $$Y \subseteq L$$. In the past, multi-label classification was mainly motivated by the tasks of text categorization and medical diagnosis. Nowadays, we notice that multi-label classification methods are increasingly required by modern applications, such as protein function classification, music categorization and semantic scene classification.