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The Usefulness of Roughly Balanced Bagging for Complex and High-Dimensional Imbalanced Data

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New Frontiers in Mining Complex Patterns (NFMCP 2015)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9607))

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Abstract

Under-sampling generalizations of bagging ensembles improve classification of imbalanced data better than other ensembles. Roughly Balanced Bagging is the most accurate among them. In this paper, we experimentally study its properties that may influence its good performance. Results of experiments show that it can be constructed with a small number of component classifiers. However, they are less diversified than components of the standard bagging. Moreover, its good performance comes from its ability to recognize unsafe types of minority examples better than other ensembles. We also present how to improve its performance by integrating bootstrap sampling with the random selection of attributes.

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Acknowledgements

The research was supported by NCN grant DEC-2013/11/B/ST6/00963.

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Authors and Affiliations

  1. Institute of Computing Science, Poznań University of Technology, 60-965, Poznań, Poland

    Mateusz Lango & Jerzy Stefanowski

Authors
  1. Mateusz Lango
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  2. Jerzy Stefanowski
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Corresponding author

Correspondence to Jerzy Stefanowski .

Editor information

Editors and Affiliations

  1. Università degli Studi di Bari Aldo Moro, Bari, Italy

    Michelangelo Ceci

  2. Università degli Studi di Bari Aldo Moro, Bari, Italy

    Corrado Loglisci

  3. ICAR-CNR, Rende, Italy

    Giuseppe Manco

  4. ICAR-CNR, Rende, Italy

    Elio Masciari

  5. University of North Carolina, Charlotte, North Carolina, USA

    Zbigniew W. Ras

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Lango, M., Stefanowski, J. (2016). The Usefulness of Roughly Balanced Bagging for Complex and High-Dimensional Imbalanced Data. In: Ceci, M., Loglisci, C., Manco, G., Masciari, E., Ras, Z. (eds) New Frontiers in Mining Complex Patterns. NFMCP 2015. Lecture Notes in Computer Science(), vol 9607. Springer, Cham. https://doi.org/10.1007/978-3-319-39315-5_7

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  • DOI: https://doi.org/10.1007/978-3-319-39315-5_7

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-39314-8

  • Online ISBN: 978-3-319-39315-5

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