Exploitation of label relationship in multi-label learning

Traditional supervised learning deals with problems where one instance is associated with a single class label, whereas in many real tasks, one instance may be associated with multiple class labels simultaneously; for example, an image can be tagged with several keywords, a document may belong to multiple topics, etc. Thus, multi-label learning has attracted great attention [3, 8, 10, 11, 12, 14, 15].