Advertisement

Exploiting Instance Relationship for Effective Extreme Multi-label Learning

  • Feifei Li
  • Hongyan Liu
  • Jun He
  • Xiaoyong Du
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10828)

Abstract

Extreme multi-label classification is an important data mining technique, which can be used to label each unseen instance with a subset of labels from a large label set. It has wide applications and many methods have been proposed in recent years. Existing methods either seek to compress label space or train a classifier based on instances’ features, among which tree-based classifiers enjoy the advantages of better efficiency and accuracy. In many real world applications, instances are not independent and relationship between instances is very useful information. However, how to utilize relationship between instances in extreme multi-label classification is less studied. Exploiting such relationship may help improve prediction accuracy, especially in the circumstance that feature space is very sparse. In this paper, we study how to utilize the similarity between instances to build more accurate tree-based extreme multi-label classifiers. To this end, we introduce the utilization of relationship between instances to state-of-the-art models in two ways: feature engineering and collaborative labeling. Extensive experiments conducted on three real world datasets demonstrate that our proposed method achieves higher accuracy than the state-of-the-art models.

Notes

Acknowledgment

This work was supported in part by National Natural Science Foundation of China under grant No. U1711262, 71771131, 71272029, 71490724 and 61472426.

References

  1. 1.
    Agrawal, R., Gupta, A., Prabhu, Y., Varma, M.: 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, pp. 13–24. ACM (2013)Google Scholar
  2. 2.
    Beygelzimer, A., Langford, J., Lifshits, Y., Sorkin, G., Strehl, A.: Conditional probability tree estimation analysis and algorithms. Eprint Arxiv, pp. 51–58 (2009)Google Scholar
  3. 3.
    Bhatia, K., Jain, H., Kar, P., Varma, M., Jain, P.: Sparse local embeddings for extreme multi-label classification. In: Cortes, C., Lawrence, N.D., Lee, D.D., Sugiyama, M., Garnett, R. (eds.) Advances in Neural Information Processing Systems vol. 28, pp. 730–738. Curran Associates, Inc. (2015)Google Scholar
  4. 4.
    Bi, W., Kwok, J.: Efficient multi-label classification with many labels. In: International Conference on Machine Learning, pp. 405–413 (2013)Google Scholar
  5. 5.
    Dembczynski, K., Cheng, W., Hllermeier, E.: Bayes optimal multilabel classification via probabilistic classifier chains. In: International Conference on Machine Learning, pp. 279–286 (2010)Google Scholar
  6. 6.
    Duchi, J., Singer, Y.: Efficient online and batch learning using forward backward splitting. J. Mach. Learn. Res. 10(18), 2899–2934 (2009)MathSciNetzbMATHGoogle Scholar
  7. 7.
    Getoor, L.: Introduction to Statistical Relational Learning. MIT press, Cambridge (2007)zbMATHGoogle Scholar
  8. 8.
    He, W., Liu, H., He, J., Tang, S., Du, X.: Extracting interest tags for non-famous users in social network. In: Proceedings of the 24th ACM International on Conference on Information and Knowledge Management, pp. 861–870. ACM (2015)Google Scholar
  9. 9.
    Jain, H., Prabhu, Y., Varma, M.: Extreme multi-label loss functions for recommendation, tagging, ranking & other missing label applications. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 935–944. ACM (2016)Google Scholar
  10. 10.
    Jain, P., Meka, R., Dhillon, I.S.: Guaranteed rank minimization via singular value projection. In: Advances in Neural Information Processing Systems, pp. 937–945 (2010)Google Scholar
  11. 11.
    Jasinska, K., Ski, K.D., Busa-Fekete, R., Pfannschmidt, K., Klerx, T., Llermeier, E.H.: Extreme f-measure maximization using sparse probability estimates. In: ICML (2016)Google Scholar
  12. 12.
    Lewis, D.D., Yang, Y., Rose, T.G., Li, F.: RCV1: a new benchmark collection for text categorization research. J. Mach. Learn. Res. 5(Apr), 361–397 (2004)Google Scholar
  13. 13.
    Lin, Z., Ding, G., Hu, M., Wang, J.: Multi-label classification via feature-aware implicit label space encoding. In: ICML, pp. 325–333 (2014)Google Scholar
  14. 14.
    Macskassy, S.A., Provost, F.: A simple relational classifier. Technical report, DTIC Document (2003)Google Scholar
  15. 15.
    McPherson, M., Smith-Lovin, L., Cook, J.M.: Birds of a feather: homophily in social networks. Ann. Rev. Sociol. 27(1), 415–444 (2001)CrossRefGoogle Scholar
  16. 16.
    Prabhu, Y., Varma, M.: Fastxml: a fast, accurate and stable tree-classifier for extreme multi-label learning. In: Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2014, pp. 263–272. ACM, New York (2014)Google Scholar
  17. 17.
    Tai, F., Lin, H.T.: Multilabel classification with principal label space transformation. Neural Comput. 24(9), 2508–2542 (2012)MathSciNetCrossRefGoogle Scholar
  18. 18.
    Tang, L., Liu, H.: Relational learning via latent social dimensions. In: Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 817–826. ACM (2009)Google Scholar
  19. 19.
    Tang, L., Liu, H.: Leveraging social media networks for classification. Data Min. Knowl. Discov. 23(3), 447–478 (2011)MathSciNetCrossRefGoogle Scholar
  20. 20.
    Wang, X., Sukthankar, G.: Multi-label relational neighbor classification using social context features. In: Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 464–472. ACM (2013)Google Scholar
  21. 21.
    Wang, Y., Wang, L., Li, Y., He, D., Liu, T.Y., Chen, W.: A theoretical analysis of NDCG type ranking measures. arXiv preprint arXiv:1304.6480 (2013)
  22. 22.
    Yen, I.E.H., Huang, X., Ravikumar, P., Zhong, K., Dhillon, I.: PD-sparse: a primal and dual sparse approach to extreme multiclass and multilabel classification. In: International Conference on Machine Learning, pp. 3069–3077 (2016)Google Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Key Laboratory of DEKE (MOE), School of InformationRenmin University of ChinaBeijingChina
  2. 2.School of Economics and ManagementTsinghua UniversityBeijingChina

Personalised recommendations