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Evaluating Difficulty of Multi-class Imbalanced Data

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Foundations of Intelligent Systems (ISMIS 2017)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10352))

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Abstract

Multi-class imbalanced classification is more difficult than its binary counterpart. Besides typical data difficulty factors, one should also consider the complexity of relations among classes. This paper introduces a new method for examining the characteristics of multi-class data. It is based on analyzing the neighbourhood of the minority class examples and on additional information about similarities between classes. The experimental study has shown that this method is able to identify the difficulty of class distribution and that the estimated minority example safe levels are related with prediction errors of standard classifiers.

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Notes

  1. 1.

    Note that in our proposal of similarity between classes, we do not model directly misclassifications between minority classes, which alternatively could be handled by yet another approach with costs of misclassifications between classes.

  2. 2.

    Refer to [9] for details of the neighborhood construction, recommended distance functions and neighborhood size tuning.

  3. 3.

    Artificial datasets and more detailed results with additional evaluation measures are available at www.cs.put.poznan.pl/mlango/publications/multi-typology.html.

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Acknowledgment

The research was funded by the Polish National Science Center, grant no. DEC-2013/11/B/ST6/00963. The work of the last author was also, partially supported by DS internal grant of PUT.

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Authors and Affiliations

  1. Institute of Computing Science, Poznan University of Technology, Poznań, Poland

    Mateusz Lango, Krystyna Napierala & Jerzy Stefanowski

Authors
  1. Mateusz Lango
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  2. Krystyna Napierala
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  3. Jerzy Stefanowski
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Corresponding author

Correspondence to Mateusz Lango .

Editor information

Editors and Affiliations

  1. Warsaw University of Technology, Warsaw, Poland

    Marzena Kryszkiewicz

  2. University of Bari Aldo Moro, Bari, Italy

    Annalisa Appice

  3. Institute of Informatics, University of Warsaw, Warsaw, Poland

    Dominik Ślęzak

  4. Faculty of Electronics & Information, Warsaw University of Technology, Warsaw, Poland

    Henryk Rybinski

  5. Institute of Mathematics, Warsaw University, Warsaw, Poland

    Andrzej Skowron

  6. Department of Computer Science, University of North Carolina at Charlotte, North Carolina, USA

    Zbigniew W. Raś

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Lango, M., Napierala, K., Stefanowski, J. (2017). Evaluating Difficulty of Multi-class Imbalanced Data. In: Kryszkiewicz, M., Appice, A., Ślęzak, D., Rybinski, H., Skowron, A., Raś, Z. (eds) Foundations of Intelligent Systems. ISMIS 2017. Lecture Notes in Computer Science(), vol 10352. Springer, Cham. https://doi.org/10.1007/978-3-319-60438-1_31

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  • DOI: https://doi.org/10.1007/978-3-319-60438-1_31

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-60437-4

  • Online ISBN: 978-3-319-60438-1

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