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Parallel GEP Ensemble for Classifying Big Datasets

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Computational Collective Intelligence (ICCCI 2018)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 11056))

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Abstract

The paper describes a GEP-based ensemble classifier constructed using the stacked generalization concept. The classifier has been implemented with a view to enable parallel processing, with the use of Spark and SWIM - an open source genetic programming library. The classifier has been validated in computational experiments carried-out on benchmark datasets.

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Author information

Authors and Affiliations

  1. Institute of Informatics, Faculty of Mathematics, Physics and Informatics, University of Gdańsk, 80-308, Gdańsk, Poland

    Joanna Jȩdrzejowicz

  2. Department of Information Systems, Gdynia Maritime University, Morska 81-83, 81-225, Gdynia, Poland

    Piotr Jȩdrzejowicz & Izabela Wierzbowska

Authors
  1. Joanna Jȩdrzejowicz
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  2. Piotr Jȩdrzejowicz
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  3. Izabela Wierzbowska
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Corresponding author

Correspondence to Izabela Wierzbowska .

Editor information

Editors and Affiliations

  1. Faculty of Information Technology, Ton Duc Thang University, Ho Chi Minh City, Vietnam

    Ngoc Thanh Nguyen

  2. Department of Computer Science and Creative Technologies, University of the West of England, Bristol, United Kingdom

    Elias Pimenidis

  3. Department of Computer Science and Creative Technologies, University of the West of England, Bristol, United Kingdom

    Zaheer Khan

  4. Faculty of Computer Science and Management, Wrocław University of Science and Technology, Wrocław, Poland

    Bogdan Trawiński

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Jȩdrzejowicz, J., Jȩdrzejowicz, P., Wierzbowska, I. (2018). Parallel GEP Ensemble for Classifying Big Datasets. In: Nguyen, N., Pimenidis, E., Khan, Z., Trawiński, B. (eds) Computational Collective Intelligence. ICCCI 2018. Lecture Notes in Computer Science(), vol 11056. Springer, Cham. https://doi.org/10.1007/978-3-319-98446-9_22

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  • DOI: https://doi.org/10.1007/978-3-319-98446-9_22

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-98445-2

  • Online ISBN: 978-3-319-98446-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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