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SMOTE Algorithm Variations in Balancing Data Streams

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Intelligent Data Engineering and Automated Learning – IDEAL 2019 (IDEAL 2019)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 11872))

Abstract

From one year to another, more and more vast amounts of data is being created in different fields of application. Great deal of those sources require real-time processing and analyzing, which leads to increased interest in streaming data classification field of machine learning. It is not rare, that many of those applications deal with somehow skewed or imbalanced data. In this paper, we analyze usage of smote oversampling algorithm variations in learning patterns from imbalanced data streams using different incremental learning ensemble algorithms.

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Notes

  1. 1.

    https://github.com/bgulowaty/stream-learn/tree/master-thesis.

  2. 2.

    https://github.com/bgulowaty/smote-paper.

  3. 3.

    https://github.com/bgulowaty/smote-paper.

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Acknowledgments

This work is supported by the Polish National Science Center under the Grant no. UMO-2015/19/B/ST6/01597 as well the statutory funds of the Department of Systems and Computer Networks, Faculty of Electronics, Wrocław University of Science and Technology.

Author information

Authors and Affiliations

  1. Department of Systems and Computer Networks, Wroclaw University of Science and Technology, Wroclaw, Poland

    Bogdan Gulowaty & Paweł Ksieniewicz

Authors
  1. Bogdan Gulowaty
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  2. Paweł Ksieniewicz
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Corresponding author

Correspondence to Bogdan Gulowaty .

Editor information

Editors and Affiliations

  1. University of Manchester, Manchester, UK

    Hujun Yin

  2. Technical University of Madrid, Madrid, Spain

    David Camacho

  3. University of Birmingham, Birmingham, UK

    Peter Tino

  4. University of Huelva, Huelva, Spain

    Antonio J. Tallón-Ballesteros

  5. University of Exeter, Exeter, UK

    Ronaldo Menezes

  6. University of Manchester, Manchester, UK

    Richard Allmendinger

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Gulowaty, B., Ksieniewicz, P. (2019). SMOTE Algorithm Variations in Balancing Data Streams. In: Yin, H., Camacho, D., Tino, P., Tallón-Ballesteros, A., Menezes, R., Allmendinger, R. (eds) Intelligent Data Engineering and Automated Learning – IDEAL 2019. IDEAL 2019. Lecture Notes in Computer Science(), vol 11872. Springer, Cham. https://doi.org/10.1007/978-3-030-33617-2_31

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  • DOI: https://doi.org/10.1007/978-3-030-33617-2_31

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-33616-5

  • Online ISBN: 978-3-030-33617-2

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