Mapreduce based selective Neural Network Ensembles using GA

被引：0
|
作者：
Wang, Zhengyu [1 ]
Xiao, Nanfeng [1 ]
机构：
[1] School of Computer Science and Engineering, South China University of Technology, Guangzhou
来源：
Journal of Information and Computational Science | 2015年 / 12卷 / 02期
基金：
中国国家自然科学基金;
关键词：
Ensemble Learning; Genetic Algorithm; Mapreduce; Neural Network Ensembles; Two-spiral Problem;
D O I：
10.12733/jics20105314
中图分类号：
学科分类号：
摘要：
Neural Network Ensembles (NNE) is proved an efficient method in both regression and classification. NNE is suitable for parallel environment owing to its computing construction. A Mapreduce based Selective Neural Network Ensembles algorithm using GA (MSNNE-GA) is proposed in this paper. MSNNE-GA uses Mapreduce to train member neural networks and also implements the GA on Mapreduce framework to select member networks for NNE. Some experiments have been done on the two-spiral classification problem. The result shows the effect of decreasing the MSE and increasing the accuracy rate of NNE with MSNNE-GA. The time-consuming of the whole process can be reduced by MSNNE-GA with the linear increasing of speed-up rate. At last the ability and sensitivity of different algorithms for big scale NNE is discussed. Comparing Bagging, MSNNE-GA can more effectively make use of the great number of member neural network to optimize the result of NNE. Copyright © 2015 Binary Information Press.
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页码：693 / 701
页数：8
相关论文
共 21 条
  • [1] Dean J., Ghemawat S., Mapreduce: Simplified data processing on large clusters, Community ACM, 51, pp. 107-113, (2008)
  • [2] Thomas Dietterich G., Ensemble Learning, The Handbook of Brain Theory and Neural Networks: Second Edition, (2002)
  • [3] An J., Zhu J., Yuan X., Multiple neural network ensembles method based on learning knowledge database, Journal of Central South University, 40, Z1, pp. 1-9, (2009)
  • [4] Hansen L.K., Salamon P., Neural network ensembles, IEEE Trans Pattern Analysis and Machine Intelligence, 12, pp. 993-1001, (1990)
  • [5] Sollich P., Krogh A., Learning with ensembles: How over fitting can be useful, Neural Information Processing System, pp. 190-196, (1996)
  • [6] Huang Y., Zhang G., Speech emotion recognition research based on the stacked generalization ensemble neural network for robot pet, Chinese Conference on Pattern Recognition, pp. 1-5, (2009)
  • [7] Kalyan K.D., Sultan U.A., A paper currency recognition system using negatively correlated neural network ensemble, Journal of Multimedia, 5, pp. 560-567, (2010)
  • [8] Wang Z., Xiao N., Using MD-AdaBoost to enhance classifier of facial expression recognition, Journal of Computational Information Systems, 9, 3, pp. 923-932, (2013)
  • [9] Liu Y., Yeang Z., Zhang B., Computational grid based neural network ensemble learning platform and its application, ICMeCG, 61, pp. 176-181, (2009)
  • [10] Breiman L., Bagging predictors, Machine Learning, 24, 2, pp. 123-140, (1996)
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