Skip to main content
SpringerLink
Log in
Book cover

International Conference on Computer Recognition Systems

CORES 2017: Proceedings of the 10th International Conference on Computer Recognition Systems CORES 2017 pp 412–422Cite as

Permutation-Based Diversity Measure for Classifier-Chain Approach

  • Conference paper
  • First Online:

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 578))

Abstract

In this paper, the problem of multilabel classification using the classifier chain scheme is addressed. We deal with the problem of building a diverse ensemble of the classifier-chain-based ensemble. For this purpose, we propose a permutation-based criterion of chain diversity. The final ensemble is build using a multi-objective genetic algorithm, which is used to optimise classification quality and chain diversity simultaneously. The proposed methods were evaluated using 29 benchmark datasets. The comparison was performed using four different multi-label evaluation measures. The experimental study reveals that the proposed approach provides a better classification quality than response-based diversity criteria.

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   169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   219.99
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

Notes

  1. 1.

    https://github.com/ptrajdos/mlWorkHorse.

References

  1. Burkhardt, S., Kramer, S.: On the spectrum between binary relevance and classifier chains in multi-label classification. In: Proceedings of the 30th Annual ACM Symposium on Applied Computing - SAC 2015. Association for Computing Machinery (ACM) (2015)

    Google Scholar 

  2. Charte, F., Rivera, A., Jesus, M.J., Herrera, F.: Concurrence among imbalanced labels and its influence on multilabel resampling algorithms. In: Polycarpou, M., Carvalho, A.C.P.L.F., Pan, J.-S., Woźniak, M., Quintian, H., Corchado, E. (eds.) HAIS 2014. LNCS, vol. 8480, pp. 110–121. Springer, Cham (2014). doi:10.1007/978-3-319-07617-1_10

    Chapter  Google Scholar 

  3. Chekina, L., Gutfreund, D., Kontorovich, A., Rokach, L., Shapira, B.: Exploiting label dependencies for improved sample complexity. Mach. Learn. 91(1), 1–42 (2012)

    Article  MathSciNet  MATH  Google Scholar 

  4. Czogalla, J., Fink, A.: Fitness landscape analysis for the resource constrained project scheduling problem. In: Stützle, T. (ed.) LION 2009. LNCS, vol. 5851, pp. 104–118. Springer, Heidelberg (2009). doi:10.1007/978-3-642-11169-3_8

    Chapter  Google Scholar 

  5. D’Ambros, M., Lanza, M., Robbes, R.: An extensive comparison of bug prediction approaches. In: 2010 7th IEEE Working Conference on Mining Software Repositories (MSR 2010). Institute of Electrical & Electronics Engineers (IEEE) (2010). http://dx.doi.org/10.1109/MSR.2010.5463279

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

    MathSciNet  MATH  Google Scholar 

  7. Garcia, S., Herrera, F.: An extension on “statistical comparisons of classifiers over multiple data sets” for all pairwise comparisons. J. Mach. Learn. Res. 9, 2677–2694 (2008)

    MATH  Google Scholar 

  8. Gibaja, E., Ventura, S.: Multi-label learning: a review of the state of the art and ongoing research. WIREs Data Min. Knowl. Discov. 4(6), 411–444 (2014)

    Article  Google Scholar 

  9. Gonçalves, E.C., Plastino, A., Freitas, A.A.: Simpler is better. In: Proceedings of the 2015 on Genetic and Evolutionary Computation Conference - GECCO 2015. Association for Computing Machinery (ACM) (2015). http://dx.doi.org/10.1145/2739480.2754650

  10. Hadka, D.: http://moeaframework.org/, http://moeaframework.org/. Accessed 9 Jan 2017

  11. Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., Witten, I.H.: The WEKA data mining software. SIGKDD Explor. Newsl. 11(1), 10 (2009)

    Article  Google Scholar 

  12. Heider, D., Senge, R., Cheng, W., Hullermeier, E.: Multilabel classification for exploiting cross-resistance information in HIV-1 drug resistance prediction. Bioinformatics 29(16), 1946–1952 (2013)

    Article  Google Scholar 

  13. Jiang, J.Y., Tsai, S.C., Lee, S.J.: FSKNN: multi-label text categorization based on fuzzy similarity and k nearest neighbors. Expert Syst. Appl. 39(3), 2813–2821 (2012)

    Article  Google Scholar 

  14. Kuncheva, L.I.: Combining Pattern Classifiers: Methods and Algorithms, 1st edn. Wiley, New York (2004)

    Book  MATH  Google Scholar 

  15. Li, H., Zhang, Q.: Multiobjective optimization problems with complicated pareto sets, MOEA/d and NSGA-II. IEEE Trans. Evol. Comput. 13(2), 284–302 (2009). http://dx.doi.org/10.1109/TEVC.2008.925798

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

    Article  Google Scholar 

  17. Peng, Y., Fang, M., Wang, C., Xie, J.: Entropy chain multi-label classifiers for traditional medicine diagnosing parkinson’s disease. In: 2015 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). Institute of Electrical and Electronics Engineers (IEEE), November 2015

    Google Scholar 

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

    Google Scholar 

  19. Read, J., Martino, L., Luengo, D.: Efficient monte carlo methods for multi-dimensional learning with classifier chains. Pattern Recogn. 47(3), 1535–1546 (2014)

    Article  MATH  Google Scholar 

  20. Read, J., Martino, L., Olmos, P.M., Luengo, D.: Scalable multi-output label prediction: from classifier chains to classifier trellises. Pattern Recogn. 48(6), 2096–2109 (2015)

    Article  Google Scholar 

  21. Read, J., Peter, R.: Meka: http://meka.sourceforge.net/, http://meka.sourceforge.net/. Accessed 29 Mar 2015

  22. Read, J., Pfahringer, B., Holmes, G., Frank, E.: Classifier chains for multi-label classification. Mach. Learn. 85(3), 333–359 (2011)

    Article  MathSciNet  Google Scholar 

  23. 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, NY, USA, pp. 705–714. ACM, New York (2011)

    Google Scholar 

  24. Sucar, L.E., Bielza, C., Morales, E.F., Hernandez-Leal, P., Zaragoza, J.H., Larrañaga, P.: Multi-label classification with bayesian network-based chain classifiers. Pattern Recogn. Lett. 41, 14–22 (2014)

    Article  Google Scholar 

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

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

    Article  Google Scholar 

  27. Tsoumakas, G., Spyromitros-Xioufis, E., Vilcek, J., Vlahavas, I.: Mulan: a java library for multi-label learning. J. Mach. Learn. Res. 12, 2411–2414 (2011). http://dl.acm.org/citation.cfm?id=1953048.2021078

    MathSciNet  MATH  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)

    Article  Google Scholar 

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

    Article  MATH  Google Scholar 

  30. 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 

  31. 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 

  32. Zhou, Z.H., Zhang, M.L.: Multi-instance multilabel learning with application to scene classification. In: Advances in Neural Information Processing Systems 19 (2007)

    Google Scholar 

  33. 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 

Download references

Acknowledgement

This work was supported by the statutory funds of the Department of Systems and Computer Networks, Wroclaw University of Science and Technology. Computational resources were provided by PL-Grid Infrastructure.

Author information

Authors and Affiliations

  1. Department of Systems and Computer Networks, Wroclaw University of 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. Department of Systems and Computer Networks, Wrocław University of Technology, Wrocław, Poland

    Marek Kurzynski

  2. Department of Systems and Computer Networks, Wrocław University of Technology, Wroclaw, Poland

    Michal Wozniak

  3. Department of Systems and Computer Networks, Wrocław University of Technology , Wroclaw, Poland

    Robert Burduk

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer International Publishing AG

About this paper

Cite this paper

Trajdos, P., Kurzynski, M. (2018). Permutation-Based Diversity Measure for Classifier-Chain Approach. In: Kurzynski, M., Wozniak, M., Burduk, R. (eds) Proceedings of the 10th International Conference on Computer Recognition Systems CORES 2017. CORES 2017. Advances in Intelligent Systems and Computing, vol 578. Springer, Cham. https://doi.org/10.1007/978-3-319-59162-9_43

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-59162-9_43

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-59161-2

  • Online ISBN: 978-3-319-59162-9

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation