Advertisement

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)

Abstract

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.

Notes

Acknowledgement

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

References

  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).  https://doi.org/10.1007/978-3-319-01595-8_18CrossRefGoogle 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).  https://doi.org/10.1007/978-3-319-55753-3_25CrossRefGoogle 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