期刊
SOFT COMPUTING
卷 16, 期 9, 页码 1493-1502出版社
SPRINGER
DOI: 10.1007/s00500-012-0824-6
关键词
Extreme learning machine; Dynamic ensemble; AdaBoost; Bagging; Sample entropy
资金
- national natural science foundation of China [61170040]
- natural science foundation of Hebei Province [F2010000323, F2011201063]
- Key Scientific Research Foundation of Education Department of Hebei Province [ZD2010139]
- natural science foundation of Hebei University [2011-228]
Extreme learning machine (ELM) as a new learning algorithm has been proposed for single-hidden layer feed-forward neural networks, ELM can overcome many drawbacks in the traditional gradient-based learning algorithm such as local minimal, improper learning rate, and low learning speed by randomly selecting input weights and hidden layer bias. However, ELM suffers from instability and over-fitting, especially on large datasets. In this paper, a dynamic ensemble extreme learning machine based on sample entropy is proposed, which can alleviate to some extent the problems of instability and over-fitting, and increase the prediction accuracy. The experimental results show that the proposed approach is robust and efficient.
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