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Introduction
Multiclass classification is an important problem in machine learning. It can be used in a variety of applications, such as organizing documents to different categories automatically. Multi-label classification is an extension of multi-class classification — the former allows a set of labels to be associated with an instance while the latter allows only one. For instance, a document may belong to both the “politics” and “health” class if it is about the National Health Insurance. Many other similar applications arise in domains like text mining, vision, or bio-informatics.
paper
Farbound Tai and Hsuan-Tien Lin. Multi-label Classification with Principal Label Space Transformation. Neural Computation, to appear.
link
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
thesis
Chun-Sung Ferng. Multi-label Classification with Hard-/soft-decoded Error-correcting Codes. Master's Thesis, 2012.
Chen-Wei Hung. Multi-label Active Learning with Auxiliary Learner. Master's Thesis, 2011.