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International Conference on Intelligent Systems Design and Applications

ISDA 2018 2018: Intelligent Systems Design and Applications pp 662–672Cite as

A Single Program Multiple Data Algorithm for Feature Selection

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Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 940))

Abstract

Feature selection is a critical component in data science and has been the topic of research for many years. Advances in hardware and the availability of better multiprocessing platforms have enabled parallel computing to reach very high levels of performance. Minimum Redundancy Maximum Relevance (mRMR) is a powerful feature selection technique used in many applications. In this paper, we present a novel optimized Single Program Multiple Data (SPMD) approach to implement the mRMR algorithm with synchronous computation, optimum load balancing and greater speedup than task-parallel approaches. The experimental results presented using multiple synthesized datasets prove the efficiency and scalability of the proposed technique over original mRMR.

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Author information

Authors and Affiliations

  1. Department of Information Technology, National Institute of Technology Karnataka, Surathkal, 575 025, India

    Bhabesh Chanduka, Tushaar Gangavarapu & C. D. Jaidhar

Authors
  1. Bhabesh Chanduka
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  2. Tushaar Gangavarapu
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  3. C. D. Jaidhar
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Corresponding author

Correspondence to Bhabesh Chanduka .

Editor information

Editors and Affiliations

  1. Machine Intelligence Research Labs, Auburn, WA, USA

    Ajith Abraham

  2. School of Information Technology and Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu, India

    Aswani Kumar Cherukuri

  3. Tijuana Institute of Technology, Tijuana, Mexico

    Patricia Melin

  4. Machine Intelligence Research Labs, Auburn, WA, USA

    Niketa Gandhi

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Chanduka, B., Gangavarapu, T., Jaidhar, C.D. (2020). A Single Program Multiple Data Algorithm for Feature Selection. In: Abraham, A., Cherukuri, A.K., Melin, P., Gandhi, N. (eds) Intelligent Systems Design and Applications. ISDA 2018 2018. Advances in Intelligent Systems and Computing, vol 940. Springer, Cham. https://doi.org/10.1007/978-3-030-16657-1_62

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