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MapReduce Implementation of a Multinomial and Mixed Naive Bayes Classifier

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  • Sikha Bagui

    (The University of West Florida, USA)

  • Keerthi Devulapalli

    (University of West Florida, USA)

  • Sharon John

    (University of West Florida, USA)

Abstract

This study presents an efficient way to deal with discrete as well as continuous values in Big Data in a parallel Naïve Bayes implementation on Hadoop's MapReduce environment. Two approaches were taken: (i) discretizing continuous values using a binning method; and (ii) using a multinomial distribution for probability estimation of discrete values and a Gaussian distribution for probability estimation of continuous values. The models were analyzed and compared for performance with respect to run time and classification accuracy for varying data sizes, data block sizes, and map memory sizes.

Suggested Citation

  • Sikha Bagui & Keerthi Devulapalli & Sharon John, 2020. "MapReduce Implementation of a Multinomial and Mixed Naive Bayes Classifier," International Journal of Intelligent Information Technologies (IJIIT), IGI Global, vol. 16(2), pages 1-23, April.
    • Handle: RePEc:igg:jiit00:v:16:y:2020:i:2:p:1-23
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    File URL: http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/IJIIT.2020040101
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    Cited by:

    1. Ahmad Alenezi, 2024. "Online Surveillance of IoT Agents in Smart Cities Using Deep Reinforcement Learning," International Journal of Intelligent Information Technologies (IJIIT), IGI Global, vol. 20(1), pages 1-15, January.

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