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Big Data: A Parallel Particle Swarm Optimization-Back-Propagation Neural Network Algorithm Based on MapReduce

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  • Jianfang Cao
  • Hongyan Cui
  • Hao Shi
  • Lijuan Jiao

Abstract

A back-propagation (BP) neural network can solve complicated random nonlinear mapping problems; therefore, it can be applied to a wide range of problems. However, as the sample size increases, the time required to train BP neural networks becomes lengthy. Moreover, the classification accuracy decreases as well. To improve the classification accuracy and runtime efficiency of the BP neural network algorithm, we proposed a parallel design and realization method for a particle swarm optimization (PSO)-optimized BP neural network based on MapReduce on the Hadoop platform using both the PSO algorithm and a parallel design. The PSO algorithm was used to optimize the BP neural network’s initial weights and thresholds and improve the accuracy of the classification algorithm. The MapReduce parallel programming model was utilized to achieve parallel processing of the BP algorithm, thereby solving the problems of hardware and communication overhead when the BP neural network addresses big data. Datasets on 5 different scales were constructed using the scene image library from the SUN Database. The classification accuracy of the parallel PSO-BP neural network algorithm is approximately 92%, and the system efficiency is approximately 0.85, which presents obvious advantages when processing big data. The algorithm proposed in this study demonstrated both higher classification accuracy and improved time efficiency, which represents a significant improvement obtained from applying parallel processing to an intelligent algorithm on big data.

Suggested Citation

  • Jianfang Cao & Hongyan Cui & Hao Shi & Lijuan Jiao, 2016. "Big Data: A Parallel Particle Swarm Optimization-Back-Propagation Neural Network Algorithm Based on MapReduce," PLOS ONE, Public Library of Science, vol. 11(6), pages 1-17, June.
    • Handle: RePEc:plo:pone00:0157551
    DOI: 10.1371/journal.pone.0157551
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    References listed on IDEAS

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    1. Yu, Feng & Xu, Xiaozhong, 2014. "A short-term load forecasting model of natural gas based on optimized genetic algorithm and improved BP neural network," Applied Energy, Elsevier, vol. 134(C), pages 102-113.
    2. Chiroma, Haruna & Abdulkareem, Sameem & Herawan, Tutut, 2015. "Evolutionary Neural Network model for West Texas Intermediate crude oil price prediction," Applied Energy, Elsevier, vol. 142(C), pages 266-273.
    3. Haruna Chiroma & Sameem Abdul-kareem & Abdullah Khan & Nazri Mohd Nawi & Abdulsalam Ya’u Gital & Liyana Shuib & Adamu I Abubakar & Muhammad Zubair Rahman & Tutut Herawan, 2015. "Global Warming: Predicting OPEC Carbon Dioxide Emissions from Petroleum Consumption Using Neural Network and Hybrid Cuckoo Search Algorithm," PLOS ONE, Public Library of Science, vol. 10(8), pages 1-21, August.
    4. Younes Saadi & Iwan Tri Riyadi Yanto & Tutut Herawan & Vimala Balakrishnan & Haruna Chiroma & Anhar Risnumawan, 2016. "Ringed Seal Search for Global Optimization via a Sensitive Search Model," PLOS ONE, Public Library of Science, vol. 11(1), pages 1-31, January.
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    Cited by:

    1. Shikha Agarwal & Prabhat Ranjan, 2018. "MR-TP-QFPSO: map reduce two phases quantum fuzzy PSO for feature selection," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 9(4), pages 888-900, August.

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