Skip to main content
SpringerLink
Log in

Dynamic Classifier Chains for Multi-label Learning

  • Conference paper
  • First Online:
Pattern Recognition (DAGM GCPR 2019)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 11824))

Included in the following conference series:

Abstract

In this paper, we deal with the task of building a dynamic ensemble of chain classifiers for multi-label classification. To do so, we proposed two concepts of the classifier chain algorithms that are able to change the label order of the chain without rebuilding the entire model. Such models allow anticipating the instance-specific chain order without the significant increase in the computational burden. The proposed chain models are built using the Naive Bayes classifier and nearest neighbour approaches. To take the benefits of the proposed algorithms, we developed a simple heuristic that allows the system to find relatively good label order. The experimental results showed that the proposed models and the heuristic are efficient tools for building dynamic chain classifiers.

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. Alvares Cherman, E., Metz, J., Monard, M.C.: A simple approach to incorporate label dependency in multi-label classification. In: Sidorov, G., Hernández Aguirre, A., Reyes García, C.A. (eds.) MICAI 2010. LNCS (LNAI), vol. 6438, pp. 33–43. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-16773-7_3

    Chapter  Google Scholar 

  2. Charte, F., Rivera, A.J., del Jesus, M.J., Herrera, F.: Quinta: a question tagging assistant to improve the answering ratio in electronic forums. In: IEEE EUROCON 2015 - International Conference on Computer as a Tool (EUROCON). IEEE, September 2015. https://doi.org/10.1109/eurocon.2015.7313677

  3. Chen, B., Li, W., Zhang, Y., Hu, J.: Enhancing multi-label classification based on local label constraints and classifier chains. In: 2016 International Joint Conference on Neural Networks (IJCNN). IEEE, July 2016. https://doi.org/10.1109/ijcnn.2016.7727370

  4. Cortes, C., Vapnik, V.: Support-vector networks. Mach. Learn. 20(3), 273–297 (1995). https://doi.org/10.1007/bf00994018

    Article  MATH  Google Scholar 

  5. Cover, T., Hart, P.: Nearest neighbor pattern classification. IEEE Trans. Inf. Theory 13(1), 21–27 (1967). https://doi.org/10.1109/tit.1967.1053964

    Article  MATH  Google Scholar 

  6. Demšar, J.: Statistical comparisons of classifiers over multiple data sets. J. Mach. Learn. Res. 7, 1–30 (2006)

    MathSciNet  MATH  Google Scholar 

  7. Dhar, M.: On cardinality of fuzzy sets. Int. J. Intell. Syst. Appl. 5(6), 47–52 (2013). https://doi.org/10.5815/ijisa.2013.06.06

    Article  Google Scholar 

  8. Garcí, V., Sánchez, J., Mollineda, R.: On the effectiveness of preprocessing methods when dealing with different levels of class imbalance. Knowl.-Based Syst. 25(1), 13–21 (2012). https://doi.org/10.1016/j.knosys.2011.06.013

    Article  Google Scholar 

  9. Gibaja, E., Ventura, S.: Multi-label learning: a review of the state of the art and ongoing research. Wiley Interdisc. Rev. Data Min. Knowl. Discov. 4(6), 411–444 (2014). https://doi.org/10.1002/widm.1139

    Article  Google Scholar 

  10. Goncalves, E.C., Plastino, A., Freitas, A.A.: A genetic algorithm for optimizing the label ordering in multi-label classifier chains. In: 2013 IEEE 25th International Conference on Tools with Artificial Intelligence. IEEE, November 2013. https://doi.org/10.1109/ictai.2013.76

  11. Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., Witten, I.H.: The weka data mining software. ACM SIGKDD Explor. Newslett. 11(1), 10 (2009). https://doi.org/10.1145/1656274.1656278

    Article  Google Scholar 

  12. Hall, M.A.: Correlation-based feature selection for machine learning. Ph.D. thesis, The University of Waikato (1999)

    Google Scholar 

  13. Hand, D.J., Yu, K.: Idiot’s Bayes: not so stupid after all? Int. Stat. Rev./Revue Internationale de Statistique 69(3), 385 (2001). https://doi.org/10.2307/1403452

    Article  MATH  Google Scholar 

  14. Huang, J., Li, G., Wang, S., Zhang, W., Huang, Q.: Group sensitive classifier chains for multi-label classification. In: 2015 IEEE International Conference on Multimedia and Expo (ICME). IEEE, June 2015. https://doi.org/10.1109/icme.2015.7177400

  15. Liu, X., Shi, Z., Li, Z., Wang, X., Shi, Z.: Sorted label classifier chains for learning images with multi-label. In: Proceedings of the International Conference on Multimedia - MM 2010. ACM Press (2010). https://doi.org/10.1145/1873951.1874121

  16. Luaces, O., Díez, J., Barranquero, J., del Coz, J.J., Bahamonde, A.: Binary relevance efficacy for multilabel classification. Progress in Artif. Intell. 1(4), 303–313 (2012). https://doi.org/10.1007/s13748-012-0030-x

    Article  Google Scholar 

  17. Madjarov, G., Kocev, D., Gjorgjevikj, D., Džeroski, S.: An extensive experimental comparison of methods for multi-label learning. Pattern Recogn. 45(9), 3084–3104 (2012). https://doi.org/10.1016/j.patcog.2012.03.004

    Article  Google Scholar 

  18. Montañes, E., et al.: Dependent binary relevance models for multi-label classification. Pattern Recogn. 47(3), 1494–1508 (2014). https://doi.org/10.1016/j.patcog.2013.09.029

    Article  Google Scholar 

  19. Quinlan, J.R.: C4.5: Programs for Machine Learning. Morgan Kaufmann Publishers Inc., San Francisco (1993)

    Google Scholar 

  20. Read, J., Martino, L., Luengo, D.: Efficient Monte Carlo methods for multi-dimensional learning with classifier chains. Pattern Recogn. 47(3), 1535–1546 (2014). https://doi.org/10.1016/j.patcog.2013.10.006

    Article  MATH  Google Scholar 

  21. Read, J., Peter, R.: (2017). http://meka.sourceforge.net/

  22. Read, J., Pfahringer, B., Holmes, G., Frank, E.: Classifier chains for multi-label classification. Mach. Learn. 85(3), 333–359 (2011). https://doi.org/10.1007/s10994-011-5256-5

    Article  MathSciNet  Google Scholar 

  23. da Silva, P.N., Gonçalves, E.C., Plastino, A., Freitas, A.A.: Distinct chains for different instances: an effective strategy for multi-label classifier chains. In: Calders, T., Esposito, F., Hüllermeier, E., Meo, R. (eds.) ECML PKDD 2014. LNCS (LNAI), vol. 8725, pp. 453–468. Springer, Heidelberg (2014). https://doi.org/10.1007/978-3-662-44851-9_29

    Chapter  Google Scholar 

  24. Spyromitros-Xioufis, E., Tsoumakas, G., Groves, W., Vlahavas, I.: Multi-target regression via input space expansion: treating targets as inputs. Mach. Learn. 104(1), 55–98 (2016). https://doi.org/10.1007/s10994-016-5546-z

    Article  MathSciNet  MATH  Google Scholar 

  25. Tomás, J.T., Spolaôr, N., Cherman, E.A., Monard, M.C.: A framework to generate synthetic multi-label datasets. Electron. Notes Theoret. Comput. Sci. 302, 155–176 (2014). https://doi.org/10.1016/j.entcs.2014.01.025

    Article  Google Scholar 

  26. Trajdos, P., Kurzynski, M.: Naive bayes classifier for dynamic chaining approach in multi-label learning. Int. J. Educ. Learn. Syst. 2, 133–142 (2017)

    Google Scholar 

  27. Trajdos, P., Kurzynski, M.: Permutation-based diversity measure for classifier-chain approach. In: Kurzynski, M., Wozniak, M., Burduk, R. (eds.) CORES 2017. AISC, vol. 578, pp. 412–422. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-59162-9_43

    Chapter  Google Scholar 

  28. Wu, J.S., Huang, S.J., Zhou, Z.H.: Genome-wide protein function prediction through multi-instance multi-label learning. IEEE/ACM Trans. Comput. Biol. Bioinform. 11(5), 891–902 (2014). https://doi.org/10.1109/tcbb.2014.2323058

    Article  Google Scholar 

  29. Xu, J.: Fast multi-label core vector machine. Pattern Recogn. 46(3), 885–898 (2013). https://doi.org/10.1016/j.patcog.2012.09.003

    Article  MATH  Google Scholar 

  30. Zhang, P., Yang, Y., Zhu, X.: Approaching multi-dimensional classification by using Bayesian network chain classifiers. In: 2014 Sixth International Conference on Intelligent Human-Machine Systems and Cybernetics. IEEE, August 2014. https://doi.org/10.1109/ihmsc.2014.129

  31. Zhou, Z.H., Zhang, M.L., Huang, S.J., Li, Y.F.: Multi-instance multi-label learning. Artif. Intell. 176(1), 2291–2320 (2012). https://doi.org/10.1016/j.artint.2011.10.002

    Article  MathSciNet  MATH  Google Scholar 

Download references

Acknowledgments

This work is financed from Grant For Young Scientists and PhD Students Development, under agreement: 0402/0109/18.

Author information

Authors and Affiliations

  1. Department of Systems and Computer Networks, Wroclaw University of Science and Technology, Wybrzeze Wyspianskiego 27, 50-370, Wroclaw, Poland

    Pawel Trajdos & Marek Kurzynski

Authors
  1. Pawel Trajdos
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Marek Kurzynski
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Pawel Trajdos .

Editor information

Editors and Affiliations

  1. TU Dortmund University, Dortmund, Germany

    Gernot A. Fink

  2. University of Hamburg, Hamburg, Germany

    Simone Frintrop

  3. University of Münster, Münster, Germany

    Xiaoyi Jiang

1 Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary material 1 (pdf 116 KB)

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Trajdos, P., Kurzynski, M. (2019). Dynamic Classifier Chains for Multi-label Learning. In: Fink, G., Frintrop, S., Jiang, X. (eds) Pattern Recognition. DAGM GCPR 2019. Lecture Notes in Computer Science(), vol 11824. Springer, Cham. https://doi.org/10.1007/978-3-030-33676-9_40

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-33676-9_40

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-33675-2

  • Online ISBN: 978-3-030-33676-9

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation