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Memetic Evolution of Classification Ensembles

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Applications of Evolutionary Computation (EvoApplications 2019)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 11454))

Abstract

Creating classification ensembles may be perceived as a regularization technique which aims at improving the generalization capabilities of a classifier. In this paper, we introduce a multi-level memetic algorithm for evolving classification ensembles (they can be either homo- or heterogeneous). First, we evolve the content of such ensembles, and then we optimize the weights (both for the classifiers and for different classes) exploited while voting. The experimental study showed that our memetic algorithm retrieves high-quality heterogeneous ensembles, and can effectively deal with small training sets in multi-class classification.

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Notes

  1. 1.

    Note that the pool of base classifiers can be either homogeneous or heterogeneous, hence include various models trained over different training sets, and they can be parameterized differently.

  2. 2.

    Available at: https://www.kaggle.com/ruslankl/mice-protein-expression.

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Acknowledgments

This work was supported by the National Science Centre, Poland, under Research Grant No. DEC-2017/25/B/ST6/00474, and JN was partially supported by the Silesian University of Technology under the Grant for young researchers (BKM-556/RAU2/2018).

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Authors and Affiliations

  1. Silesian University of Technology, Gliwice, Poland

    Szymon Piechaczek, Michal Kawulok & Jakub Nalepa

Authors
  1. Szymon Piechaczek
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  2. Michal Kawulok
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  3. Jakub Nalepa
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Corresponding author

Correspondence to Jakub Nalepa .

Editor information

Editors and Affiliations

  1. University of Mainz, Mainz, Germany

    Paul Kaufmann

  2. University of Granada, Granada, Spain

    Pedro A. Castillo

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Piechaczek, S., Kawulok, M., Nalepa, J. (2019). Memetic Evolution of Classification Ensembles. In: Kaufmann, P., Castillo, P. (eds) Applications of Evolutionary Computation. EvoApplications 2019. Lecture Notes in Computer Science(), vol 11454. Springer, Cham. https://doi.org/10.1007/978-3-030-16692-2_20

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  • DOI: https://doi.org/10.1007/978-3-030-16692-2_20

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-16691-5

  • Online ISBN: 978-3-030-16692-2

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