Extracting Label Importance Information for Multi-label Classification

  • Dengbao Wang
  • Li LiEmail author
  • Jingyuan Wang
  • Fei Hu
  • Xiuzhen Zhang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10828)


Existing multi-label learning approaches assume all labels in a dataset are of the same importance. However, the importance of each label is generally different in real world. In this paper, we introduce multi-label importance (MLI) which measures label importance from two perspectives: label predictability and label effects. Specifically, label predictability and label effects can be extracted from training data before building models for multi-label learning. After that, the multi-label importance information can be used in existing approaches to improve the performance of multi-label learning. To prove this, we propose a classifier chain algorithm based on multi-label importance ranking and a improved kNN-based algorithm which takes both feature distance and label distance into consideration. We apply our algorithms on benchmark datasets demonstrating efficient multi-label learning by exploiting multi-label importance. It is also worth mentioning that our experiments show the strong positive correlation between label predictability and label effects.



It was supported by NSF Chongqing China (cstc2017zdcy-zdyf0366).


  1. 1.
    Boutell, M.R., Luo, J., Shen, X., Brown, C.M.: Learning multi-label scene classification. Pattern Recognit. 37(9), 1757–1771 (2004)CrossRefGoogle Scholar
  2. 2.
    Singer, Y., Schapire, R.E.: BoosTexter: a boosting-based system for text categorization. Mach. Learn. 39, 135–168 (2000)CrossRefGoogle Scholar
  3. 3.
    Elisseeff, A., Weston, J.: A kernel method for multi-labelled classification. In: Proceedings of NIPS, vol. 14, pp. 681–687 (2001)Google Scholar
  4. 4.
    Read, J., Pfahringer, B., Holmes, G., Frank, E.: Classifier chains for multi-label classification. Mach. Learn. 85(3), 333 (2011)MathSciNetCrossRefGoogle Scholar
  5. 5.
    Zhang, M.L., Zhou, Z.H.: A review on multi-label learning algorithms. IEEE Trans. Knowl. Data Eng. 26(8), 1819–1837 (2014)CrossRefGoogle Scholar
  6. 6.
    Tsoumakas, G., Katakis, I., Vlahavas, I.: Mining multi-label data. In: Maimon, O., Rokach, L. (eds.) Data Mining and Knowledge Discovery Handbook, pp. 667–685. Springer, Boston (2009)CrossRefGoogle Scholar
  7. 7.
    Brinker, K.: Multilabel classification via calibrated label ranking. Mach. Learn. 73(2), 133–153 (2008)CrossRefGoogle Scholar
  8. 8.
    Tsoumakas, G., Katakis, I., Taniar, D.: Multi-label classification: an overview. Int. J. Data Warehous. Min. 3(3), 1–13 (2007)CrossRefGoogle Scholar
  9. 9.
    Zhang, M.L., Zhou, Z.H.: Ml-KNN: a lazy learning approach to multi-label learning. Pattern Recogn. 40(7), 2038–2048 (2007)CrossRefGoogle Scholar
  10. 10.
    Zhang, M.L., Zhou, Z.H.: Multilabel neural networks with applications to functional genomics and text categorization. IEEE Trans. Knowl. Data Eng. 18(10), 1338–1351 (2006)CrossRefGoogle Scholar
  11. 11.
    Li, Y.K., Zhang, M.L., Geng, X.: Leveraging implicit relative labeling-importance information for effective multi-label learning. In: IEEE International Conference on Data Mining, vol. 6, pp. 251–260. IEEE (2016)Google Scholar
  12. 12.
    Geng, X., Ji, R.: Label distribution learning. In: IEEE International Conference on Data Mining Workshops, pp. 377–383. IEEE Computer Society (2013)Google Scholar
  13. 13.
    Hamming, R.W.: Error detecting and error correcting codes. Bell Syst. Tech. J. 29(2), 147–160 (1982)MathSciNetCrossRefGoogle Scholar
  14. 14.
    Plastino, A., Freitas, A.A.: A genetic algorithm for optimizing the label ordering in multi-label classifier chains. In: IEEE International Conference on TOOLS with Artificial Intelligence, pp. 469–476. IEEE Computer Society (2013)Google Scholar
  15. 15.
    Klimt, B., Yang, Y.: Introducing the Enron Corpus. In: Conference on Email and Anti-Spam. DBLP (2004)Google Scholar
  16. 16.
    Zhang, L., Zhang, Y., Tang, J., Lu, K., Tian, Q.: Binary code ranking with weighted hamming distance. In: 2013 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), vol. 9, pp. 1586–1593. IEEE (2013)Google Scholar
  17. 17.
    Wu, X. Z., Zhou, Z. H.: A Unified View of Multi-Label Performance Measures. arXiv preprint arXiv: 1609.00288 (2016)
  18. 18.
    Trohidis, K., Tsoumakas, G., Kalliris, G., Vlahavas, I.: Multilabel classification of music into emotions. Blood 90(9), 3438–3443 (2008)Google Scholar
  19. 19.
    Yu, Y., Pedrycz, W., Miao, D.: Multi-label classification by exploiting label correlations. Expert Syst. App. 41(6), 2989–3004 (2014)CrossRefGoogle Scholar
  20. 20.
    Senge, R., del Coz, J.J., Hüllermeier, E.: On the problem of error propagation in classifier chains for multi-label classification. In: Spiliopoulou, M., Schmidt-Thieme, L., Janning, R. (eds.) Data Analysis, Machine Learning and Knowledge Discovery. SCDAKO, pp. 163–170. Springer, Cham (2014). Scholar
  21. 21.
    Hu, F., Xu, X., Wang, J., Yang, Z., Li, L.: Memory-enhanced latent semantic model: short text understanding for sentiment analysis. In: Candan, S., Chen, L., Pedersen, T.B., Chang, L., Hua, W. (eds.) DASFAA 2017. LNCS, vol. 10177, pp. 393–407. Springer, Cham (2017). Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Dengbao Wang
    • 1
  • Li Li
    • 1
    Email author
  • Jingyuan Wang
    • 1
  • Fei Hu
    • 1
  • Xiuzhen Zhang
    • 2
  1. 1.College of Computer and Information ScienceSouthwest UniversityChongqingChina
  2. 2.School of Computer Science and Information TechnologyRMIT UniversityMelbourneAustralia

Personalised recommendations