Skip to main content
SpringerLink
Log in
Book cover

Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing pp 207–217Cite as

Fuzzy Rough Decision Trees for Multi-label Classification

  • Conference paper
  • First Online:

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9437))

Abstract

Multi-label classification exists widely in medical analysis or image annotation. Although there are some algorithms to train models for multi-label classification, few of them are able to extract comprehensible rules. In this paper, we propose a multi-label decision tree algorithm based on fuzzy rough sets, named ML-FRDT. This method can tackle with symbolic, continuous and fuzzy data. We conduct experiments on two multi-label datasets. And the experiment results show that ML-FRDT achieves good performance than some well-established multi-label classification algorithms.

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

References

  1. McCallum., A.: Multi-label text classification with a mixture model trained by EM. In: AAAI\({^{\prime }}\)99 Workshop on Text Learning, pp. 1–7 (1999)

    Google Scholar 

  2. Lewis, D.D., Yang, Y., Rose, T.G., Li, F.: Rcv1: A new benchmark collection for text categorization research. J. Mach. Learn. Res. 5, 361–397 (2004)

    Google Scholar 

  3. Gao, S., Wu, W., Lee C.H., et al.: A MFoM learning approach to robust multiclass multi-label text categorization. In: Proceedings of the Twenty-first International Conference on Machine Learning (ICML 2004), pp. 42. ACM (2004)

    Google Scholar 

  4. Boutell, M.R., Luo, J., Shen, X., Brown, C.M.: Learning multi-label scene classification. Pattern Recogn. 37, 1757–1771 (2004)

    Article  Google Scholar 

  5. Wu, F., Han, Y., Tian, Q., Zhuang, Y.: Multi-label boosting for image annotation by structural grouping sparsity. In: Proceedings of the International Conference on Multimedia (MM 2010), pp. 15–24. ACM (2010)

    Google Scholar 

  6. Wang, Z., Hu, Y., Chia, L.T.: Multi-label learning by image-to-class distance for scene classification and image annotation. In: Proceedings of the ACM International Conference on Image and Video Retrieval (CIVR 2010), pp. 105–112. ACM (2010)

    Google Scholar 

  7. Snoek, C.G., Worring, M., Van Gemert, J.C., Geusebroek, J.M., Smeulders, A.W.: 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. ACM (2006)

    Google Scholar 

  8. Sanden, C., Zhang, J.Z.: Enhancing multi-label music genre classification through ensemble techniques. In: Proceedings of the 34th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2011), pp. 705–714. ACM (2011)

    Google Scholar 

  9. Zhang, M., Zhou, Z.: A review on multi-label learning algorithms. IEEE Trans. Knowl. Data Eng. 26, 1819–1837 (2013)

    Article  Google Scholar 

  10. Clare, A.J., King, R.D.: Knowledge discovery in multi-label phenotype data. In: Siebes, A., De Raedt, L. (eds.) PKDD 2001. LNCS (LNAI), vol. 2168, pp. 42–53. Springer, Heidelberg (2001)

    Chapter  MATH  Google Scholar 

  11. Zhang, M.L., Zhou, Z.H.: ML-KNN: A lazy learning approach to multi-label learning. Pattern Recogn. 40, 2038–2048 (2007)

    Article  MATH  Google Scholar 

  12. Zhang, M.L.: ML-RBF: RBF neural networks for multi-label learning. Neural Process. Lett. 29, 61–74 (2009)

    Article  Google Scholar 

  13. Dubois, D., Prade, H.: Rough fuzzy sets and fuzzy rough sets\({^{*}}\). Int. J. Gen. Syst. 17, 191–209 (1990)

    Article  MATH  Google Scholar 

  14. Bhatt, R.B., Gopal, M.: FRCT: fuzzy-rough classification trees. Pattern Anal. Appl. 11, 73–88 (2008)

    Article  MathSciNet  Google Scholar 

  15. An, S., Hu, Q.: Fuzzy rough decision trees. In: Yao, J.T., Yang, Y., Słowiński, R., Greco, S., Li, H., Mitra, S., Polkowski, L. (eds.) RSCTC 2012. LNCS, vol. 7413, pp. 397–404. Springer, Heidelberg (2012)

    Chapter  Google Scholar 

  16. Zhang, L., Hu, Q., Duan, J., Wang, X.: Multi-label feature selection with fuzzy rough sets. In: Miao, D., Pedrycz, W., Slezak, D., Peters, G., Hu, Q., Wang, R. (eds.) RSKT 2014. LNCS, vol. 8818, pp. 121–128. Springer, Heidelberg (2014)

    Chapter  Google Scholar 

  17. Quinlan, J.R.: C4. 5: Programs for Machine Learning. Elsevier, Burlington (1993)

    Google Scholar 

  18. Godbole, S., Sarawagi, S.: Discriminative methods for multi-labeled classification. In: Dai, H., Srikant, R., Zhang, C. (eds.) PAKDD 2004. LNCS (LNAI), vol. 3056, pp. 22–30. Springer, Heidelberg (2004)

    Chapter  Google Scholar 

  19. Correa, G.E., Plastino, A., Freitas, A.A.: A genetic algorithm for optimizing the label ordering in multi-label classifier chains. In: IEEE 25th International Conference on Tools with Artificial Intelligence (ICTAI) 2013, pp. 469–476. IEEE (2013)

    Google Scholar 

  20. Briggs, F., Huang, Y., Raich, R., Eftaxias, K., Lei, Z., Cukierski, W., et al.: The 9th annual MLSP competition: new methods for acoustic classification of multiple simultaneous bird species in a noisy environment. In: IEEE International Workshop on Machine Learning for Signal Processing (MLSP) 2013, pp. 1–8. IEEE (2013)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Computer Science and Technology, Tianjin University, Tianjin, 300072, China

    Xiaoxue Wang, Shuang An, Hong Shi & Qinghua Hu

Authors
  1. Xiaoxue Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Shuang An
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Hong Shi
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Qinghua Hu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Qinghua Hu .

Editor information

Editors and Affiliations

  1. University of Regina, Regina, SK, Canada

    Yiyu Yao

  2. Tianjin University, Tianjin, China

    Qinghua Hu

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

    Hong Yu

  4. University of Kansas, Lawrence, KS, USA

    Jerzy W. Grzymala-Busse

Rights and permissions

Reprints and permissions

Copyright information

© 2015 Springer International Publishing Switzerland

About this paper

Cite this paper

Wang, X., An, S., Shi, H., Hu, Q. (2015). Fuzzy Rough Decision Trees for Multi-label Classification. In: Yao, Y., Hu, Q., Yu, H., Grzymala-Busse, J.W. (eds) Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing. Lecture Notes in Computer Science(), vol 9437. Springer, Cham. https://doi.org/10.1007/978-3-319-25783-9_19

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-25783-9_19

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-25782-2

  • Online ISBN: 978-3-319-25783-9

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation