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Evolutionary Algorithms for Fast Parallel Classification

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Proceedings of the 9th International Conference on Computer Recognition Systems CORES 2015

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 403))

Abstract

The classification tries to assign the best category to given unknown records based on previous observations. It is clear that with the growing amount of data, any classification algorithm can be very slow. The learning speed of many developed state-of-the-art algorithms like deep neural networks or support vector machines is very low. Evolutionary-based approaches in classification have the same problem. This paper describes five different evolutionary-based approaches that solve the classification problem and run in real time. This was achieved by using GPU parallelization. These classifiers are evaluated on two collections that contains millions of records. The proposed parallel approach is much faster and preserve the same precision as a serial version.

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Notes

  1. 1.

    http://www.nvidia.com/object/cuda_home_new.html.

  2. 2.

    http://www.open-mpi.org/.

  3. 3.

    https://docs.it4i.cz/anselm-cluster-documentation.

  4. 4.

    http://archive.ics.uci.edu/ml/datasets/HIGGS.

  5. 5.

    http://archive.ics.uci.edu/ml/datasets/SUSY.

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Acknowledgments

This work was supported by the IT4Innovations Centre of Excellence project (CZ.1.05/1.1.00/02.0070), funded by the European Regional Development Fund and the national budget of the Czech Republic via the Research and Development for Innovations Operational Programme and by Project SP2015/105 “DPDM—Database of Performance and Dependability Models” of the Student Grand System, VŠB—Technical University of Ostrava and by Project SP2015/146 “Parallel processing of Big data 2” of the Student Grand System, VŠB—Technical University of Ostrava.

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Authors and Affiliations

  1. Department of Computer Science, FEECS, VŠB-Technical University of Ostrava, 17. listopadu, 708 33, Ostrava-Poruba, Czech Republic

    Tomáš Ježowicz, Petr Buček, Jan Platoš & Václav Snášel

  2. IT4Innovations, VŠB-Technical University of Ostrava, 17. listopadu 15/2172, 708 33, Ostrava-Poruba, Czech Republic

    Tomáš Ježowicz, Petr Buček, Jan Platoš & Václav Snášel

Authors
  1. Tomáš Ježowicz
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  2. Petr Buček
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  3. Jan Platoš
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  4. Václav Snášel
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Corresponding author

Correspondence to Tomáš Ježowicz .

Editor information

Editors and Affiliations

  1. Department of Systems, Wrocław University of Technology, Wroclaw, Poland

    Robert Burduk

  2. Department of Systems and Computer, Wrocław University of Technology, Wroclaw, Poland

    Konrad Jackowski

  3. Department of Systems and Computer, Wrocław University of Technology, Wroclaw, Poland

    Marek Kurzyński

  4. Dept. of Systems and Computer Networks, Wrocław University of Technology, Wroclaw, Poland

    Michał Woźniak

  5. Department of Systems, Wrocław University of Technology, Wroclaw, Poland

    Andrzej Żołnierek

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Ježowicz, T., Buček, P., Platoš, J., Snášel, V. (2016). Evolutionary Algorithms for Fast Parallel Classification. In: Burduk, R., Jackowski, K., Kurzyński, M., Woźniak, M., Żołnierek, A. (eds) Proceedings of the 9th International Conference on Computer Recognition Systems CORES 2015. Advances in Intelligent Systems and Computing, vol 403. Springer, Cham. https://doi.org/10.1007/978-3-319-26227-7_62

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  • DOI: https://doi.org/10.1007/978-3-319-26227-7_62

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