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Feature Selection in High-Dimensional Dataset Using MapReduce

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Artificial Intelligence (BNAIC 2017)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 823))

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Abstract

This paper describes a distributed MapReduce implementation of the minimum Redundancy Maximum Relevance algorithm, a popular feature selection method in bioinformatics and network inference problems. The proposed approach handles both tall/narrow and wide/short datasets. We further provide an open source implementation based on Hadoop/Spark, and illustrate its scalability on datasets involving millions of observations or features.

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Acknowledgement

The author CR acknowledges the funding of the BridgeIris project (RBC/13-PFS EH-11) supported by INNOVIRIS (Brussels Institute for the encouragement of scientific research and innovation) and The Belgian Kids’ Fund. The authors YLB and GB acknowledge the funding of the Brufence project (Scalable machine learning for automating defense system) supported by INNOVIRIS (Brussels Institute for the encouragement of scientific research and innovation).

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Authors and Affiliations

  1. Machine Learning Group, Faculty of Science, Université Libre de Bruxelles, Boulevard du Triomphe, CP 212, 1050, Brussels, Belgium

    Claudio Reggiani, Yann-Aël Le Borgne & Gianluca Bontempi

Authors
  1. Claudio Reggiani
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  2. Yann-Aël Le Borgne
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  3. Gianluca Bontempi
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Correspondence to Claudio Reggiani .

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Editors and Affiliations

  1. Artificial Intelligence, University of Groningen, Groningen, The Netherlands

    Bart Verheij

  2. Artificial Intelligence, University of Groningen, Groningen, The Netherlands

    Marco Wiering

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Reggiani, C., Le Borgne, YA., Bontempi, G. (2018). Feature Selection in High-Dimensional Dataset Using MapReduce. In: Verheij, B., Wiering, M. (eds) Artificial Intelligence. BNAIC 2017. Communications in Computer and Information Science, vol 823. Springer, Cham. https://doi.org/10.1007/978-3-319-76892-2_8

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  • DOI: https://doi.org/10.1007/978-3-319-76892-2_8

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  • Online ISBN: 978-3-319-76892-2

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