Skip to main content
SpringerLink
Log in
Book cover

International Conference on Database Systems for Advanced Applications

DASFAA 2017: Database Systems for Advanced Applications pp 265–278Cite as

Fast Extended One-Versus-Rest Multi-label SVM Classification Algorithm Based on Approximate Extreme Points

  • Conference paper
  • First Online:

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 10177))

Abstract

In large-scale multi-label classification framework, applications of non-linear kernel extended one-versus-rest multi-label support vector machine (OVR-ESVM) classification algorithm are severely restricted by excessive training time. To deal with this problem, we improve the OVR-ESVM classification algorithm and propose fast OVR-ESVM classification algorithm based on approximate extreme points (AEML-ESVM). The AEML-ESVM classification algorithm integrates the advantages of OVR-ESVM classification algorithm and binary approximate extreme points support vector machine (AESVM) classification algorithm. In other words, it can not only shorten the training time greatly, but also reflect label correlation of individual instance explicitly. Meanwhile, its classification performance is similar to that of the OVR-ESVM classification algorithm. Experiment results on three public data sets show that AEML-ESVM classification algorithm can substantially reduce training time and its classification performance is comparable with that of the OVR-ESVM classification algorithm. It also outperforms existing fast multi-label SVM classification algorithms in both training time and classification performance.

This is a preview of subscription content, log in via an institution.

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

References

  1. 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, Heidelberg (2009)

    Chapter  Google Scholar 

  2. Elghazel, H., Aussem, A., Gharroudi, O., Saadaoui, W.: Ensemble multi-label text categorization based on rotation forest and latent semantic indexing. Expert Syst. Appl. 57(C), 1–11 (2016)

    Article  Google Scholar 

  3. Hou, S., Zhou, S., Chen, L., Feng, Y., Awudu, K.: Multi-label learning with label relevance in advertising video. Neurocomputing 171(C), 932–948 (2016)

    Article  Google Scholar 

  4. Jing, X.Y., Wu, F., Li, Z., Hu, R., Zhang, D.: Multi-label dictionary learning for image annotation. IEEE Trans. Image Process. 25(6), 2712–2715 (2016)

    Article  MathSciNet  Google Scholar 

  5. Zufferey, D., Hofer, T., Hennebert, J., Schumacher, M., Ingold, R., Bromuri, S.: Performance comparison of multi-label learning algorithms on clinical data for chronic diseases. Comput. Biol. Med. 65(C), 34–43 (2015)

    Article  Google Scholar 

  6. Liu, Y., Liu, Y., Wang, C., Wang, X.: What strikes the strings of your heart?-multi-label dimensionality reduction for music emotion analysis via brain imaging. IEEE Trans. Autonom. Mental Dev. 7(3), 176–188 (2015)

    Article  Google Scholar 

  7. Gibaja, E., Ventura, S.: A tutorial on multi-label learning. ACM Comput. Surv. 47(3), 1–38 (2015)

    Article  Google Scholar 

  8. Xu, J.: An extended one-versus-rest support vector machine for multi-label classification. Neurocomputing 74(17), 3114–3124 (2011)

    Article  Google Scholar 

  9. Cortes, C., Vapnik, V.: Support-vector networks. Mach. Learn. 20(3), 273–297 (1995)

    MATH  Google Scholar 

  10. Nandan, M., Khargonekar, P.P., Talathi, S.S.: Fast SVM training using approximate extreme points. J. Mach. Learn. Res. 15(1), 59–98 (2014)

    MathSciNet  MATH  Google Scholar 

  11. Fürnkranz, J., Hüllermeier, E., Mencía, E.L., Brinker, K.: Multilabel classification via calibrated label ranking. Mach. Learn. 73(2), 133–153 (2008)

    Article  Google Scholar 

  12. Tsang, I.W., Kwok, J.T., Cheung, P.M.: Core vector machines: fast SVM training on very large data sets. J. Mach. Learn. Res. 6(1), 363–392 (2005)

    MathSciNet  MATH  Google Scholar 

  13. Wang, S., Wang, J., Wang, Z., Ji, Q.: Enhancing multi-label classification by modeling dependencies among labels. Pattern Recogn. 47(10), 3405–3413 (2014)

    Article  Google Scholar 

  14. Zhou, Z.H., Zhang, M.L., Huang, S.J., Li, Y.F.: Multi-instance multi-label learning. Artif. Intell. 176(1), 2291–2320 (2012)

    Article  MathSciNet  MATH  Google Scholar 

  15. Luaces, O., Díez, J., Barranquero, J., Coz, J.J., Bahamonde, A.: Binary relevance efficacy for multilabel classification. Prog. Artif. Intell. 1(4), 303–313 (2012)

    Article  Google Scholar 

  16. Clare, A., King, R.D.: Knowledge discovery in multi-label phenotype data. In: Raedt, L., Siebes, A. (eds.) PKDD 2001. LNCS (LNAI), vol. 2168, pp. 42–53. Springer, Heidelberg (2001). doi:10.1007/3-540-44794-6_4

    Chapter  Google Scholar 

  17. Elisseeff, A., Weston, J.: A kernel method for multi-labelled classification. In: Advances in Neural Information Processing Systems, pp. 681–687 (2001)

    Google Scholar 

  18. Xu, J.: Fast Multi-label core vector machine. Pattern Recogn. 46(3), 885–898 (2013)

    Article  MATH  Google Scholar 

  19. Zhang, M.L., Zhou, Z.H.: Multi-label neural networks with applications to functional genomics and text categorization. IEEE Trans. Knowl. Data Eng. 18(10), 1338–1351 (2006)

    Article  Google Scholar 

  20. Boutell, M.R., Luo, J., Shen, X., et al.: Learning multi-label scene classification. Pattern Recogn. 37(9), 1757–1771 (2004)

    Article  Google Scholar 

  21. Chang, C.C., Lin, C.J.: LIBSVM: a library for support vector machines. ACM Trans. Intell. Syst. Technol. 2(3), 27 (2011)

    Article  Google Scholar 

  22. Platt, J.C.: Fast training of support vector machines using sequential minimal optimization. In: Advances in Kernel Methods, pp. 185–208 (1999)

    Google Scholar 

  23. LIBSVM datasets. https://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/

  24. Zhang, M.L., Zhou, Z.H.: A review on multi-label learning algorithms. IEEE Trans. Knowl. Data Eng. 26(8), 1819–1837 (2014)

    Article  Google Scholar 

  25. Schapire, R.E., Singer, Y.: BoosTexter: a boosting-based system for text categorization. Mach. Learn. 39(2), 135–168 (2000)

    Article  MATH  Google Scholar 

Download references

Acknowledgments

This work is supported by the National Natural Science Foundation of China (NSFC) under the grant number 61170258, 61103196, 61379127, 61379128, 61572448, by the National Key R&D Program of China under the grant number 2016YFC1401900 and by the Shandong Provincial Natural Science Foundation of China under the grant number ZR2014JL043.

Author information

Authors and Affiliations

  1. Department of Computer Science and Technology, Ocean University of China, Qingdao, China

    Zhongwei Sun, Zhongwen Guo, Xupeng Wang, Jing Liu & Shiyong Liu

Authors
  1. Zhongwei Sun
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Zhongwen Guo
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Xupeng Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Jing Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Shiyong Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Zhongwen Guo .

Editor information

Editors and Affiliations

  1. Arizona State University , Tempe - Phoenix, Arizona, USA

    Selçuk Candan

  2. Hong Kong University of Science and Tech , Hong Kong, China

    Lei Chen

  3. Aalborg University , Aalborg, Denmark

    Torben Bach Pedersen

  4. University of New South Wales , Sydney, New South Wales, Australia

    Lijun Chang

  5. The University of Queensland , Brisbane, Queensland, Australia

    Wen Hua

Rights and permissions

Reprints and permissions

Copyright information

© 2017 Springer International Publishing AG

About this paper

Cite this paper

Sun, Z., Guo, Z., Wang, X., Liu, J., Liu, S. (2017). Fast Extended One-Versus-Rest Multi-label SVM Classification Algorithm Based on Approximate Extreme Points. In: Candan, S., Chen, L., Pedersen, T., Chang, L., Hua, W. (eds) Database Systems for Advanced Applications. DASFAA 2017. Lecture Notes in Computer Science(), vol 10177. Springer, Cham. https://doi.org/10.1007/978-3-319-55753-3_17

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-55753-3_17

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-55752-6

  • Online ISBN: 978-3-319-55753-3

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation