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Soft Computing
Published in: Soft Computing 9/2012

01-09-2012 | Focus

Dynamic ensemble extreme learning machine based on sample entropy

Authors: Jun-hai Zhai, Hong-yu Xu, Xi-zhao Wang

Published in: Soft Computing | Issue 9/2012

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Abstract

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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Metadata
Title
Dynamic ensemble extreme learning machine based on sample entropy
Authors
Jun-hai Zhai
Hong-yu Xu
Xi-zhao Wang
Publication date
01-09-2012
Publisher
Springer-Verlag
Published in
Soft Computing / Issue 9/2012
Print ISSN: 1432-7643
Electronic ISSN: 1433-7479
DOI
https://doi.org/10.1007/s00500-012-0824-6

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