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Multi-label Classification with Label Correlations of Multimedia Datasets

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Multimedia and Network Information Systems

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 506))

Abstract

In multi-label classification tasks, very often labels are correlated and to not lose important information, methods should take into account existing dependencies. Such situation especially takes place in the case of multimedia datasets. In the paper, universal problem transformation methods providing for label correlations are considered. The comparison is done for proposed by authors Labels Chain technique [4] and well known methods which also take into account label correlations, such as Label Power-set, Classifier Chains and Ensembles of Classifier Chains. The performance of the methods is examined by experiments done on image, musical, audio and text datasets.

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Author information

Authors and Affiliations

  1. Institute of Information Technology, Lodz University of Technology, Wólczańska 215, Lodz, Poland

    Kinga Glinka & Danuta Zakrzewska

Authors
  1. Kinga Glinka
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  2. Danuta Zakrzewska
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Corresponding author

Correspondence to Kinga Glinka .

Editor information

Editors and Affiliations

  1. Department of Information Systems, Faculty of Computer Science and Management, Wrocław University of Science and Technology, Wroclaw, Poland

    Aleksander Zgrzywa

  2. Department of Information Systems, Faculty of Computer Science and Management, Wrocław University of Science and Technology, Wrocław, Poland

    Kazimierz ChoroÅ›

  3. Department of Information Systems, Faculty of Computer Science and Management, Wrocław University of Science and Technology, Wrocław, Poland

    Andrzej Siemiński

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Glinka, K., Zakrzewska, D. (2017). Multi-label Classification with Label Correlations of Multimedia Datasets. In: Zgrzywa, A., Choroś, K., Siemiński, A. (eds) Multimedia and Network Information Systems. Advances in Intelligent Systems and Computing, vol 506. Springer, Cham. https://doi.org/10.1007/978-3-319-43982-2_5

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  • DOI: https://doi.org/10.1007/978-3-319-43982-2_5

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-43981-5

  • Online ISBN: 978-3-319-43982-2

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