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Obtaining Pareto Front in Instance Selection with Ensembles and Populations

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Artificial Intelligence and Soft Computing (ICAISC 2018)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10841))

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Abstract

Collective computational intelligence can be used in several ways, for example as taking the decision together by some form of a bagging ensemble or as finding the solutions by multi-objective evolutionary algorithms. In this paper we examine and compare the application of the two approaches to instance selection for creating the Pareto front of the selected subsets, where the two objectives are classification accuracy and data size reduction. As the bagging ensemble members we use DROP5 algorithms. The evolutionary algorithm is based on NSGA-II. The findings are that the evolutionary approach is faster (contrary to the popular belief) and usually provides better quality solutions, with some exceptions, were the outcome of the DROP5 ensemble is better.

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Acknowledgments

This work was supported by the NCN (Polish National Science Center) grant “Evolutionary Methods in Data Selection” No. 2017/01/X/ST6/00202.

Author information

Authors and Affiliations

  1. Department of Computer Science and Automatics, University of Bielsko-Biala, Bielsko-Biała, Poland

    Mirosław Kordos & Marcin Wydrzyński

  2. Częstochowa University of Technology, Institute of Computational Intelligence, Częstochowa, Poland

    Krystian Łapa

Authors
  1. Mirosław Kordos
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  2. Marcin Wydrzyński
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  3. Krystian Łapa
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Corresponding author

Correspondence to Mirosław Kordos .

Editor information

Editors and Affiliations

  1. Częstochowa University of Technology, Częstochowa, Poland

    Leszek Rutkowski

  2. Częstochowa University of Technology, Częstochowa, Poland

    Rafał Scherer

  3. Częstochowa University of Technology, Częstochowa, Poland

    Marcin Korytkowski

  4. University of Alberta, Edmonton, AB, Canada

    Witold Pedrycz

  5. AGH University of Science and Technology, Kraków, Poland

    Ryszard Tadeusiewicz

  6. University of Louisville, Louisville, KY, USA

    Jacek M. Zurada

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Kordos, M., Wydrzyński, M., Łapa, K. (2018). Obtaining Pareto Front in Instance Selection with Ensembles and Populations. In: Rutkowski, L., Scherer, R., Korytkowski, M., Pedrycz, W., Tadeusiewicz, R., Zurada, J. (eds) Artificial Intelligence and Soft Computing. ICAISC 2018. Lecture Notes in Computer Science(), vol 10841. Springer, Cham. https://doi.org/10.1007/978-3-319-91253-0_41

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  • DOI: https://doi.org/10.1007/978-3-319-91253-0_41

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-91252-3

  • Online ISBN: 978-3-319-91253-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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