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.

• Chun-Sung Ferng and Hsuan-Tien Lin. Multi-label Classification with Error-correcting Codes, ACML 2011. link
• Chen-Wei Hung and Hsuan-Tien Lin. Multi-label Active Learning with Auxiliary Learner, ACML 2011. link
• Farbound Tai and Hsuan-Tien Lin. Multi-label Classification with Principle Label Space Transformation. National Taiwan University, September 2010; submitted. A preliminary version appeared in MLD Workshop @ ICML '10. link, with code: snapshot on 2011/01/23
• Farbound Tai and Hsuan-Tien Lin. Multi-label Classification with Principle Label Space Transformation. Second International Workshop on learning from Multi-Label Data @ ICML '10, 2010. link