Multi-label classification

In machine learning, multi-label classification or multi-output classification is a variant of the classification problem where multiple nonexclusive labels may be assigned to each instance. Multi-label classification is a generalization of multiclass classification, which is the single-label problem of categorizing instances into precisely one of several (greater than or equal to two) classes. In the multi-label problem the labels are nonexclusive and there is no constraint on how many of the classes the instance can be assigned to. The formulation of multi-label learning was first introduced by Shen et al. in the context of Semantic Scene Classification,^[1]^[2] and later gained popularity across various areas of machine learning.

Formally, multi-label classification is the problem of finding a model that maps inputs x to binary vectors y; that is, it assigns a value of 0 or 1 for each element (label) in y.

^ Xipeng Shen, Matthew Boutell, Jiebo Luo, and Christopher Brown, "Multi-label Machine Learning and Its Application to Semantic Scene Classification", In Proceedings of IS&T/SPIE's Sixteenth Anaual Symposium on Electronic Imaging: Science and Technology (EI 2004), San Jose, California, USA, January 2004, pages 188--199.
^ Matthew R. Boutell, Jiebo Luo, Xipeng Shen and Christopher M. Brown, "Learning Multi-label Scene Classification", in Pattern Recognition, Volume 37, Issue 9, 2004, pages 1757-1771.

[multilabel-shen-1] Xipeng Shen, Matthew Boutell, Jiebo Luo, and Christopher Brown, "Multi-label Machine Learning and Its Application to Semantic Scene Classification", In Proceedings of IS&T/SPIE's Sixteenth Anaual Symposium on Electronic Imaging: Science and Technology (EI 2004), San Jose, California, USA, January 2004, pages 188--199.

[multilabel-shen-pr-2] Matthew R. Boutell, Jiebo Luo, Xipeng Shen and Christopher M. Brown, "Learning Multi-label Scene Classification", in Pattern Recognition, Volume 37, Issue 9, 2004, pages 1757-1771.

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