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

Jianfang Cao1, Hongyan Cui1, Hao Shi2

  • 1Computer Science and Technology Department, Xinzhou Teachers University, Xinzhou, China.

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|June 16, 2016
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Summary
This summary is machine-generated.

This study introduces a parallel particle swarm optimization (PSO)-optimized back-propagation (BP) neural network using MapReduce on Hadoop. The method significantly enhances classification accuracy and runtime efficiency for big data processing.

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Machine Learning

Background:

  • Back-propagation (BP) neural networks are effective for nonlinear mapping but suffer from long training times and decreased accuracy with increasing data size.
  • Processing big data with traditional BP neural networks presents challenges due to hardware and communication overhead.

Purpose of the Study:

  • To improve the classification accuracy and runtime efficiency of BP neural networks for big data.
  • To propose a parallel design and realization method for a PSO-optimized BP neural network using MapReduce on the Hadoop platform.

Main Methods:

  • Particle Swarm Optimization (PSO) was employed to optimize the initial weights and thresholds of the BP neural network.
  • The MapReduce parallel programming model on the Hadoop platform was utilized for parallel processing of the BP algorithm.
  • Datasets of varying scales were created from the SUN Database scene image library.

Main Results:

  • The parallel PSO-BP neural network achieved approximately 92% classification accuracy.
  • The system demonstrated approximately 0.85 in system efficiency, indicating significant advantages in big data processing.
  • The proposed algorithm showed substantial improvements in both classification accuracy and time efficiency.

Conclusions:

  • The parallel PSO-BP neural network effectively addresses the limitations of traditional BP networks when handling big data.
  • This approach offers a robust solution for enhancing the performance of intelligent algorithms in big data environments.
  • The integration of parallel processing with intelligent algorithms represents a significant advancement for big data analytics.