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International Conference on Artificial Neural Networks

ICANN 2017: Artificial Neural Networks and Machine Learning – ICANN 2017 pp 368–375Cite as

Two-Level Neural Network for Multi-label Document Classification

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Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 10614))

Abstract

This paper deals with multi-label document classification using neural networks. We propose a novel neural network which is composed of two sub-nets: the first one estimates the scores for all classes, while the second one determines the number of classes assigned to the document. The proposed approach is evaluated on Czech and English standard corpora. The experimental results show that the proposed method is competitive with state of the art on both languages.

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Notes

  1. 1.

    http://www.daviddlewis.com/resources/testcollections/reuters21578/.

  2. 2.

    This approach has been proposed in [7].

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Acknowledgements

This work has been supported by the project LO1506 of the Czech Ministry of Education, Youth and Sports.

Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, Faculty of Applied Sciences, University of West Bohemia, Plzeň, Czech Republic

    Ladislav Lenc & Pavel Král

  2. NTIS - New Technologies for the Information Society, Faculty of Applied Sciences, University of West Bohemia, Plzeň, Czech Republic

    Ladislav Lenc & Pavel Král

Authors
  1. Ladislav Lenc
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  2. Pavel Král
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Corresponding author

Correspondence to Ladislav Lenc .

Editor information

Editors and Affiliations

  1. University of Lausanne, Lausanne, Switzerland

    Alessandra Lintas

  2. University of Genoa, Genoa, Italy

    Stefano Rovetta

  3. Universitat Pompeu Fabra, Barcelona, Spain

    Paul F.M.J. Verschure

  4. University of Lausanne, Lausanne, Switzerland

    Alessandro E.P. Villa

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Lenc, L., Král, P. (2017). Two-Level Neural Network for Multi-label Document Classification. In: Lintas, A., Rovetta, S., Verschure, P., Villa, A. (eds) Artificial Neural Networks and Machine Learning – ICANN 2017. ICANN 2017. Lecture Notes in Computer Science(), vol 10614. Springer, Cham. https://doi.org/10.1007/978-3-319-68612-7_42

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  • DOI: https://doi.org/10.1007/978-3-319-68612-7_42

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-68611-0

  • Online ISBN: 978-3-319-68612-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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