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Effective Multi-label Classification Method for Multidimensional Datasets

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Book cover Flexible Query Answering Systems 2015

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 400))

Abstract

Multi-label classification, contrarily to the traditional single-label one, aims at predicting more than one predefined class label for data instances. Multi-label classification problems very often concern multidimensional datasets where number of attributes significantly exceeds relatively small number of instances. In the paper, new effective problem transformation method which deals with such cases is introduced. The proposed Labels Chain (LC) algorithm is based on relationship between labels, and consecutively uses result labels as new attributes in the following classification process. Experiments conducted on several multidimensional datasets showed the good performance of the presented method, taking into account predictive accuracy and computation time. The obtained results are compared with those obtained by the most popular Binary Relevance (BR) and Label Power-set (LP) algorithms.

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Author information

Authors and Affiliations

  1. Institute of Information Technology, Lodz University of Technology, Wólczańska 215, 90-924, 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. Dept. of Comm., Business & Info. Tech., Roskilde University, Roskilde, Denmark

    Troels Andreasen

  2. Dept. Computer Science, Roskilde University, Roskilde, Denmark

    Henning Christiansen

  3. Sys Res Intit of Polish Acad of Science, Intelligent Systems Laboratory, Warsaw, Poland

    Janusz Kacprzyk

  4. Department of Electronic Systems, Aalborg University, Esbjerg, Denmark

    Henrik Larsen

  5. Computer Science Department, University Milano-Biccoca, Milano, Italy

    Gabriella Pasi

  6. IRISA, University of Rennes I, Rennes, France

    Olivier Pivert

  7. Dept. of Tele. & Info. Processing, Ghent University, Gent, Belgium

    Guy De Tré

  8. Dept. of Comp. & Art. Int. Sci., University of Granada, Granada, Spain

    Maria Amparo Vila

  9. Department of Computer Engineering, Middle East Technical University, Ankara, Turkey

    Adnan Yazici

  10. Systems Research Institute, Polish Academy of Sciences, Warsaw, Poland

    Sławomir Zadrożny

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Glinka, K., Zakrzewska, D. (2016). Effective Multi-label Classification Method for Multidimensional Datasets. In: Andreasen, T., et al. Flexible Query Answering Systems 2015. Advances in Intelligent Systems and Computing, vol 400. Springer, Cham. https://doi.org/10.1007/978-3-319-26154-6_10

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  • DOI: https://doi.org/10.1007/978-3-319-26154-6_10

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-26153-9

  • Online ISBN: 978-3-319-26154-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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