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Memetic Evolution of Classification Ensembles

  • Szymon Piechaczek
  • Michal Kawulok
  • Jakub NalepaEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, 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.

Keywords

Ensemble classifier Memetic algorithm Classification 

Notes

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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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Silesian University of TechnologyGliwicePoland

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