Multi-label classification is an extension of the traditional multi-class classification—the former allows a set of labels to be associated with an instance while the latter allows only one. Applications of multi-label classification naturally arise in domains such as text mining, vision, or bio-informatics. For instance, a document is usually associated with more than one category; a picture often includes many objects; a gene is usually multi-functional.