Dynamic ensemble extreme learning machine based on sample entropy

Authors:
Jun-hai Zhai;Hong-yu Xu;Xi-zhao Wang
Affiliations:
Hebei University, Key Laboratory of Machine Learning and Computational Intelligence, College of Mathematics and Computer Science, 071002, Baoding, Hebei, China;Hebei University, Key Laboratory of Machine Learning and Computational Intelligence, College of Mathematics and Computer Science, 071002, Baoding, Hebei, China;Hebei University, Key Laboratory of Machine Learning and Computational Intelligence, College of Mathematics and Computer Science, 071002, Baoding, Hebei, China
Venue:
Soft Computing - A Fusion of Foundations, Methodologies and Applications - Special Issue on Extreme Learning Machines (ELM 2011) Hangzhou, China, December 6 – 8, 2011
Year:
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.