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Vertical and Horizontal Data Partitioning for Classifier Ensemble Learning

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Book cover Progress in Computer Recognition Systems (CORES 2019)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 977))

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Abstract

Multiple classifier systems have proven superiority over individual ones to solve classification tasks. One of the main issues in those solution relies in data size, when the amount of data to be analyzed becomes huge. In this paper, the performance of ensemble system to succeed by using only portions of the available data is analyzed. For this, extensive experimentation with homogeneous ensemble systems trained with 50% of data and 50% of features is performed, using bagging sampling schema. Simple and weighted majority voting schemes are implemented to combine the classifier outputs. Experimental results including 25 datasets show the benefit of using multiple classifiers trained on limited data. The ensemble size and the accuracy obtained with individual model trained over the entire dataset is compared.

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Notes

  1. 1.

    Package e1071: http://cran.r-project.org/web/packages/e1071/index.html.

  2. 2.

    Package partykit: http://cran.r-project.org/package=partykit.

  3. 3.

    Package nnet: http://cran.r-project.org/web/packages/nnet/index.html.

  4. 4.

    UCI MachineLearning Database Repository: http://archive.ics.uci.edu/ml/.

  5. 5.

    Statistical inference procedures: http://sci2s.ugr.es/sicidm/.

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Acknowledgements

This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No̱ 665959. In addition, Michał Woźniak was supported by the statutory funds of the Department of Systems and Computer Networks, Faculty of Electronics, Wrocław University of Science and Technology.

Author information

Authors and Affiliations

  1. Deusto University, Bilbao, Spain

    Amgad M. Mohammed & Enrique Onieva

  2. Deusto Institute of Technology (DeustoTech), Bilbao, Spain

    Amgad M. Mohammed & Enrique Onieva

  3. Menoufia University, Menoufia, Egypt

    Amgad M. Mohammed

  4. Wrocław University of Science and Technology, Wrocław, Poland

    Michał Woźniak

Authors
  1. Amgad M. Mohammed
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  2. Enrique Onieva
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  3. Michał Woźniak
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Corresponding author

Correspondence to Amgad M. Mohammed .

Editor information

Editors and Affiliations

  1. Faculty of Electronics, Wroclaw University of Science and Technology, Wrocław, Poland

    Robert Burduk

  2. Faculty of Electronics, Wroclaw University of Science and Technology, Wrocław, Poland

    Marek Kurzynski

  3. Faculty of Electronics, Wroclaw University of Science and Technology, Wrocław, Poland

    Michał Wozniak

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Mohammed, A.M., Onieva, E., Woźniak, M. (2020). Vertical and Horizontal Data Partitioning for Classifier Ensemble Learning. In: Burduk, R., Kurzynski, M., Wozniak, M. (eds) Progress in Computer Recognition Systems. CORES 2019. Advances in Intelligent Systems and Computing, vol 977. Springer, Cham. https://doi.org/10.1007/978-3-030-19738-4_10

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