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Ensemble Diversity in Evolving Data Streams

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Discovery Science (DS 2016)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9956))

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Abstract

While diversity of ensembles has been studied in the context of static data, it has not still received such research interest for evolving data streams. This paper aims at analyzing the impact of concept drift on diversity measures calculated for streaming ensembles. We consider six popular diversity measures and adapt their calculations to data stream requirements. A comprehensive series of experiments reveals the potential of each measure for visualizing ensemble performance over time. Measures highlighted as capable of depicting sudden and virtual drifts over time are used as basis for detecting changes with the Page-Hinkley test. Experimental results demonstrate that the \(\kappa \) interrater agreement, disagreement, and double fault measures, although designed to quantify diversity, provide a means of detecting changes competitive to that using classification accuracy.

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Notes

  1. 1.

    Source code, test scripts, and generator parameters available at:http://www.cs.put.poznan.pl/dbrzezinski/software.php.

  2. 2.

    http://www.cs.put.poznan.pl/dbrzezinski/software/DiversityInStream.html.

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Acknowledgments

The authors’ research was funded by the Polish National Science Center under Grant No. DEC-2013/11/B/ST6/00963. Dariusz Brzezinski acknowledges the support of an FNP START scholarship.

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Authors and Affiliations

  1. Institute of Computing Science, Poznan University of Technology, ul. Piotrowo 2, 60–965, Poznan, Poland

    Dariusz Brzezinski & Jerzy Stefanowski

Authors
  1. Dariusz Brzezinski
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  2. Jerzy Stefanowski
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Corresponding author

Correspondence to Dariusz Brzezinski .

Editor information

Editors and Affiliations

  1. Campus Middelhe, M.G.103a, Universiteit Antwerpen Campus Middelhe, M.G.103a, Antwerp, Belgium

    Toon Calders

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

    Michelangelo Ceci

  3. Bari, Italy

    Donato Malerba

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Brzezinski, D., Stefanowski, J. (2016). Ensemble Diversity in Evolving Data Streams. In: Calders, T., Ceci, M., Malerba, D. (eds) Discovery Science. DS 2016. Lecture Notes in Computer Science(), vol 9956. Springer, Cham. https://doi.org/10.1007/978-3-319-46307-0_15

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  • DOI: https://doi.org/10.1007/978-3-319-46307-0_15

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-46306-3

  • Online ISBN: 978-3-319-46307-0

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