Encyclopedia of Machine Learning and Data Mining

Living Edition
| Editors: Claude Sammut, Geoffrey I. Webb

Multi-label Learning

  • Zhi-Hua Zhou
  • Min-Ling Zhang
Living reference work entry
DOI: https://doi.org/10.1007/978-1-4899-7502-7_910-1


Multi-label learning is an important machine learning setting where each example is associated with multiple class labels simultaneously. Firstly, definition, motivation and background, and learning system structure for multi-label learning are introduced. Secondly, multi-label evaluation measures and the issue of label correlation are discussed. Thirdly, basic ideas and technical details on four representative multi-label learning algorithms are considered. Lastly, theory, extensions, and future challenges on multi-label learning are introduced.


Class Label Label Space Relevant Label Unseen Instance Label Correlation 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
This is a preview of subscription content, log in to check access.

Recommended Reading

  1. Agrawal R, Gupta A, Prabhu Y, Varma M (2013) Multi-label learning with millions of labels: recommending advertiser bid phrases for web pages. In: Proceedings of the 22nd international conference on world wide web, Rio de Janeiro, pp 13–24Google Scholar
  2. Boutell MR, Luo J, Shen X, Brown CM (2004) Learning multi-label scene classification. Pattern Recognit 37(9):1757–1771CrossRefGoogle Scholar
  3. Dembczyński K, Kotłowski W, Hüllermeier E (2012) Consistent multilabel ranking through univariate loss minimization. In: Proceedings of the 29th international conference on machine learning, Edinburgh, pp 1319–1326Google Scholar
  4. Elisseeff A, Weston J (2002) A kernel method for multi-labelled classification. In: Dietterich TG, Becker S, Ghahramani Z (eds) Advances in neural information processing systems, vol 14. MIT Press, Cambridge, pp 681–687Google Scholar
  5. Gao W, Zhou Z-H (2011) On the consistency of multi-label learning. In: Proceedings of the 24th annual conference on learning theory, Budapest, pp 341–358Google Scholar
  6. Geng X, Yin C, Zhou Z-H (2013) Facial age estimation by label distribution learning. IEEE Trans Pattern Anal Mach Intell 35(10):2401–2412CrossRefGoogle Scholar
  7. Liu L, Dietterich T (2012) A conditional multinomial mixture model for superset label learning. In: Bartlett P, Pereira FCN, Burges CJC, Bottou L, Weinberger KQ (eds) Advances in neural information processing systems, vol 25. MIT Press, Cambridge, pp 557–565Google Scholar
  8. Read J, Pfahringer B, Holmes G, Frank E (2011) Classifier chains for multi-label classification. Mach Learn 85(3):333–359MathSciNetCrossRefGoogle Scholar
  9. Rousu J, Saunders C, Szedmak S, Shawe-Taylor J (2005) Learning hierarchical multi-category text classification models. In: Proceedings of the 22nd international conference on machine learning, Bonn, pp 774–751Google Scholar
  10. Schapire RE, Singer Y (2000) Boostexter: a boosting-based system for text categorization. Mach Learn 39(2/3):135–168CrossRefMATHGoogle Scholar
  11. Tsoumakas G, Katakis I, Vlahavas I (2011) Random k-labelsets for multi-label classification. IEEE Trans Knowl Data Eng 23(7):1079–1089CrossRefGoogle Scholar
  12. Weston J, Bengio S, Usunier N (2011) WSABIE: scaling up to large vocabulary image annotation. In: Proceedings of the 22nd international joint conference on artificial intelligence, Barcelona, pp 2764–2770Google Scholar
  13. Zhang M-L, Zhou Z-H (2007) ML-kNN: a lazy learning approach to multi-label learning. Pattern Recognit 40(7):2038–2048CrossRefMATHGoogle Scholar
  14. Zhang M-L, Zhou Z-H (2014) A review on multi-label learning algorithms. IEEE Trans Knowl Data Eng 26(8):1819–1837CrossRefGoogle Scholar
  15. Zhou Z-H, Zhang M-L, Huang S-J, Li Y-F (2012) Multi-instance multi-label learning. Artif Intell 176(1):2291–2320MathSciNetCrossRefMATHGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  1. 1.National Key Laboratory for Novel Software TechnologyNanjing UniversityNanjing 210023China
  2. 2.School of Computer Science and EngineeringSoutheast UniversityNanjing 210096China