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Computational Intelligence and Neuroscience
Volume 2015, Article ID 297672, 13 pages
Research Article

MapReduce Based Parallel Neural Networks in Enabling Large Scale Machine Learning

1School of Electrical Engineering and Information, Sichuan University, Chengdu 610065, China
2The Key Laboratory of Embedded Systems and Service Computing, Tongji University, Shanghai 200092, China
3Department of Computing, Canterbury Christ Church University, Canterbury, Kent CT1 1QU, UK

Received 28 May 2015; Accepted 11 August 2015

Academic Editor: Ladislav Hluchy

Copyright © 2015 Yang Liu et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Artificial neural networks (ANNs) have been widely used in pattern recognition and classification applications. However, ANNs are notably slow in computation especially when the size of data is large. Nowadays, big data has received a momentum from both industry and academia. To fulfill the potentials of ANNs for big data applications, the computation process must be speeded up. For this purpose, this paper parallelizes neural networks based on MapReduce, which has become a major computing model to facilitate data intensive applications. Three data intensive scenarios are considered in the parallelization process in terms of the volume of classification data, the size of the training data, and the number of neurons in the neural network. The performance of the parallelized neural networks is evaluated in an experimental MapReduce computer cluster from the aspects of accuracy in classification and efficiency in computation.