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Semi-supervised Learning Algorithm for Binary Relevance Multi-label Classification

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Web Information Systems Engineering – WISE 2014 Workshops (WISE 2014)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 9051))

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Abstract

The presented paper describes our model for the WISE 2014 challenge multi-label classification task. The goal of the challenge was to implement a multi-label text classification model which maximizes the mean \(F_1\) score on a private test data. The described method involves a binary relevance scheme with linear classifiers trained using stochastic gradient descent. A novel method for determining the values of classifiers’ meta-parameters was developed. In addition, our solution employs the semi-supervised learning which significantly improves the evaluation score. The presented solution won the third place in the challenge. The results are discussed and the supervised and semi-supervised approaches are compared.

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Notes

  1. 1.

    See discussion forum at Kaggle.com: https://www.kaggle.com/c/wise-2014/forums/t/9773/our-approach-5th-place.

  2. 2.

    Similar to recommendation from sklearn documentation:

    http://scikit-learn.org/stable/modules/sgd.html.

References

  1. Fan, R., Chang, K., Hsieh, C.: LIBLINEAR: A library for large linear classification. J. Mach. Learn. 9, 1871–1874 (2008)

    MATH  Google Scholar 

  2. 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 

  3. Mairesse, F., Gašić, M., Jurčíček, F., Keizer, S., Thomson, B., Yu, K., Young, S.: Spoken language understanding from unaligned data using discriminative classification models. In: 2009 IEEE International Conference on Acoustics, Speech and Signal Processing. ICASSP 2009, pp. 4749–4752. IEEE, Taipei (2009)

    Google Scholar 

  4. Matoušek, J., Tihelka, D.: Annotation errors detection in TTS corpora. In: Proc. Interspeech. Lyon, France (2013)

    Google Scholar 

  5. Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., Duchesnay, E.: Scikit-learn: machine learning in python. J. Mach. Learn. 12, 2825–2830 (2012)

    Google Scholar 

  6. Skorkovská, L.: Dynamic threshold selection method for multi-label newspaper topic identification. In: Habernal, I. (ed.) TSD 2013. LNCS, vol. 8082, pp. 209–216. Springer, Heidelberg (2013)

    Google Scholar 

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

    Article  Google Scholar 

  8. Tsuruoka, Y., Tsujii, J., Ananiadou, S.: Stochastic gradient descent training for L1-regularized log-linear models with cumulative penalty. In: Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP, vol. 1, p. 477 (2009)

    Google Scholar 

  9. Švec, J., Šmídl, L., Ircing, P.: Hierarchical Discriminative Model for Spoken Language Understanding. In: 2013 Proceedings of IEEE International Conference on Acoustics, Speech, and Signal Processing, pp. 8322–8326. IEEE, Vancouver, Canada (2013)

    Google Scholar 

  10. Zadrozny, B., Elkan, C.: Transforming classifier scores into accurate multiclass probability estimates. In: Proceedings of the Eighth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining KDD, vol. 2, pp. 694–699 (2002)

    Google Scholar 

  11. Zhang, T.: Solving large scale linear prediction problems using stochastic gradient descent algorithms. In: Proceedings of the Twenty-First International Conference on Machine Learning ICML 2004, p. 116. ACM, New York (2004)

    Google Scholar 

  12. Zou, H., Zhu, J., Hastie, T.: New multicategory boosting algorithms based on multicategory Fisher-consistent losses. Ann. Appl. Stat. 2(4), 1290–1306 (2008)

    Article  MATH  MathSciNet  Google Scholar 

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Acknowledgments

This research was supported by the Grant Agency of the Czech Republic, project No. GAČR GBP103/12/G084. Access to computing and storage facilities owned by parties and projects contributing to the National Grid Infrastructure MetaCentrum, (project No. LM2010005), is greatly appreciated.

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Authors and Affiliations

  1. Department of Cybernetics, New Technologies for Information Society, University of West Bohemia, Univerzitní 22, 306 14, Pilsen, Czech Republic

    Jan Švec

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  1. Jan Švec
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Correspondence to Jan Švec .

Editor information

Editors and Affiliations

  1. University of New South Wales, Sydney, Australia

    Boualem Benatallah

  2. Boston University, Boston, Massachusetts, USA

    Azer Bestavros

  3. University of Genova, Genova, Italy

    Barbara Catania

  4. CSIRO ICT Centre, Canberra, Australia

    Armin Haller

  5. Aristotle University of Thessaloniki, Thessaloniki, Greece

    Yannis Manolopoulos

  6. Aristotle University of Thessaloniki, Thessaloniki, Greece

    Athena Vakali

  7. Victoria University, Footscray, Australia

    Yanchun Zhang

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Švec, J. (2015). Semi-supervised Learning Algorithm for Binary Relevance Multi-label Classification. In: Benatallah, B., et al. Web Information Systems Engineering – WISE 2014 Workshops. WISE 2014. Lecture Notes in Computer Science(), vol 9051. Springer, Cham. https://doi.org/10.1007/978-3-319-20370-6_1

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  • DOI: https://doi.org/10.1007/978-3-319-20370-6_1

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-20369-0

  • Online ISBN: 978-3-319-20370-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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