Skip to main content
SpringerLink
Log in

Three-Way Decisions Based Multi-label Learning Algorithm with Label Dependency

  • Conference paper
  • First Online:
Rough Sets (IJCRS 2016)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9920))

Included in the following conference series:

Abstract

A great number of algorithms have been proposed for multi-label learning, and these algorithms usually divide the labels with an optimal threshold according to their relevances to an unseen instance. However, it may easily cause misclassification to directly determine whether an unseen instance has the label with relevance close to the threshold. The label with relevance close to the threshold has a high uncertainty. Three-way decisions theory is an efficient method to solve the uncertainty problem. Therefore, based on three-way decisions theory, a multi-label learning algorithm with label dependency is proposed in this paper. Label dependency is an inherent property in multi-label data. The labels with high uncertainty are further handled with a label dependency model, which is represented by the logistic regression in this paper. The experimental results show that this algorithm performs better.

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

Access this chapter

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

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, New York (2010)

    Google Scholar 

  2. Tsoumakas, G., Katakis, I.: Multi label classification: an overview. Int. J. Data Warehouse. Min. 3(3), 1–13 (2007)

    Article  Google Scholar 

  3. 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 

  4. Yu, Y., Pedrycz, W., Miao, D.Q.: Neighborhood rough sets based multi-label classification for automatic image annotation. Int. J. Approximate Reason. 54(9), 1373–1387 (2013)

    Article  MATH  Google Scholar 

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

    Article  MATH  Google Scholar 

  6. Pavlidis, P., Weston, J., Cai, J., Grundy, W.N.: Combining microarray expression data and phylogenetic profiles to learn functional categories using support vector machines. In: Proceedings of the Fifth Annual International Conference on Computational Biology, Montreal, Canada, pp. 242–248 (2001)

    Google Scholar 

  7. Snoek, C.G.M., Worring, M., Van Gemert, J.C., et al.: The challenge problem for automated detection of 101 semantic concepts in multimedia. In: Proceedings of the 14th Annual ACM International Conference on Multimedia, pp. 421–430 (2006)

    Google Scholar 

  8. Yao, Y.: An outline of a theory of three-way decisions. In: Yao, J.T., Yang, Y., Słowiński, R., Greco, S., Li, H., Mitra, S., Polkowski, L. (eds.) RSCTC 2012. LNCS, vol. 7413, pp. 1–17. Springer, Heidelberg (2012). doi:10.1007/978-3-642-32115-3_1

    Chapter  Google Scholar 

  9. Pawlak, Z.: Rough sets. Int. J. Parallel Prog. 11(5), 341–356 (1982)

    MathSciNet  MATH  Google Scholar 

  10. Zhang, M.L., Zhang, K.: Multi-label learning by exploiting label dependency. In Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 999–1008. ACM, New York (2010)

    Google Scholar 

  11. Kang, F., Jin, R., Sukthankar, R.: Correlated label propagation with application to multi-label learning. In: Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 1719–1726 (2006)

    Google Scholar 

  12. Yu, Y., Predrycz, W., Miao, D.Q.: Multi-label classification by exploiting label correlations. Expert Syst. Appl. 41(6), 2989–3004 (2014)

    Article  Google Scholar 

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

    Article  Google Scholar 

  14. Hllermeier, E., Frnkranz, J., Cheng, W., et al.: Label ranking by learning pairwise preferences. Artif. Intell. 172(16), 1897–1916 (2008)

    Article  MathSciNet  Google Scholar 

  15. Tsoumakas, G., Vlahavas, I.P.: Random k-labelsets: an ensemble method for multilabel classification. In: Kok, J.N., Koronacki, J., Lopez de Mantaras, R., Matwin, S., Mladenič, D., Skowron, A. (eds.) ECML 2007. LNCS (LNAI), vol. 4701, pp. 406–417. Springer, Heidelberg (2007). doi:10.1007/978-3-540-74958-5_38

    Chapter  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, vol. 14, pp. 681–687 (2001)

    Google Scholar 

  18. 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)

    Article  Google Scholar 

  19. Yao, Y.Y.: Three-way decisions with probabilistic rough sets. Inf. Sci. 180(3), 341–353 (2010)

    Article  MathSciNet  Google Scholar 

  20. Tsoumakas, G., Spyromitros-Xiousfis, E., Vilcek, I.V.J.: Mulan: a Java library for multi-label learning. J. Mach. Learn. Res. 12(7), 2411–2414 (2011)

    MathSciNet  MATH  Google Scholar 

  21. Pestian, J., Brew, C., Matykiewicz, P., et al.: A shared task involving multi-label classification of clinical free text. In: Proceedings of the Workshop on BioNLp 2007, pp. 97–104. Association for Computational Linguistics, Stroudsburg (2007)

    Google Scholar 

  22. UC Berkeley Enron Email Analysis Project. http://bailando.sims.berkeley.edu/enron_email.html

  23. Turnbull, D., Barrington, L., Torres, D., et al.: Semantic annotation and retrieval of music and sound effects. IEEE Trans. Audio Speech Lang. Process. 16(2), 467–476 (2008)

    Article  Google Scholar 

  24. Zhang, M.L., Zhou, Z.H.: ML-kNN: a lazy learning approach to multi-label learning. Pattern Recogn. 40(7), 2038–2048 (2007)

    Article  MATH  Google Scholar 

Download references

Acknowledgments

The work is partially supported by the National Natural Science Foundation of China (Nos. 61273304, 61573259), the Specialized Research Fund for the Doctoral Program of Higher Education of China (No. 20130072130004), and the program of Further Accelerating the Development of Chinese Medicine Three Year Action of Shanghai (2014–2016) (No. ZY3-CCCX-3-6002).

Author information

Authors and Affiliations

  1. Department of Computer Science and Technology, Tongji University, Shanghai, 201804, China

    Feng Li, Duoqian Miao & Wei Zhang

  2. Key Laboratory of Embedded Systems and Service Computing, Ministry of Education, Tongji University, Shanghai, 201804, China

    Feng Li, Duoqian Miao & Wei Zhang

Authors
  1. Feng Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Duoqian Miao
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Wei Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Duoqian Miao .

Editor information

Editors and Affiliations

  1. Universidad Católica del Norte, Antofagasta, Chile

    Víctor Flores

  2. University of Campinas, Campinas, SP, Brazil

    Fernando Gomide

  3. University of Warsaw, Warsaw, Poland

    Andrzej Janusz

  4. Universidad del Católica del Norte, Antofagasta, Chile

    Claudio Meneses

  5. Tongji University, Shanghai, China

    Duoqian Miao

  6. University of Applied Sciences, Munich, Germany

    Georg Peters

  7. University of Warsaw, Warsaw, Poland

    Dominik Ślęzak

  8. Chongqing University of Posts and Telecommunications, Chongqing, China

    Guoyin Wang

  9. Universidad de Chile, Santiago, Chile

    Richard Weber

  10. University of Regina, Regina, Saskatchewan, Canada

    Yiyu Yao

Rights and permissions

Reprints and permissions

Copyright information

© 2016 Springer International Publishing AG

About this paper

Cite this paper

Li, F., Miao, D., Zhang, W. (2016). Three-Way Decisions Based Multi-label Learning Algorithm with Label Dependency. In: Flores, V., et al. Rough Sets. IJCRS 2016. Lecture Notes in Computer Science(), vol 9920. Springer, Cham. https://doi.org/10.1007/978-3-319-47160-0_22

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-47160-0_22

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-47159-4

  • Online ISBN: 978-3-319-47160-0

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation