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Dynamic ensemble extreme learning machine based on sample entropy
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AI
DOI：10.1007/s00500-012-0824-6.png)
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Abstract
En 中文
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.
Keywords:
Extreme learning machine
Dynamic ensemble
AdaBoost
Bagging
Sample entropy