Share Email Print
cover

Proceedings Paper

Evolutionary extreme learning machine based on dynamic Adaboost ensemble
Author(s): Gaitang Wang; Ping Li
Format Member Price Non-Member Price
PDF $14.40 $18.00
cover GOOD NEWS! Your organization subscribes to the SPIE Digital Library. You may be able to download this paper for free. Check Access

Paper Abstract

Boosting ensemble algorithm exhibits two fatal limitations: one is that it gives in advance the upper bound of weighted error on weak learning algorithm; the other one is that it is overdependent on data and weak learning machine, and it is too sensitive to data noising. Aimed at limitation of Boosting ensemble application in extreme learning machine, this paper proposes a new algorithm: evolutionary extreme learning machine based on dynamic Adaboost ensemble, which regards the evolutionary extreme learning machine as weak learning machine, dynamic Adaboost ensemble algorithm is used to integrate the outputs of weak learning machines, and makes use of fuzzy activation function as activation function of evolutionary extreme learning machine because of low computational burden and easy implementation in hardware. Proposed algorithm has been successfully applied to problem of function approximation and classification application. Experimental results show that the algorithm increases the training speed greatly when dealing with large dataset and has better generalization performance compared to extreme learning machine, evolutionary extreme learning machine and Boosting ensemble extreme learning machine with quasi-Newton algorithms.

Paper Details

Date Published: 20 August 2010
PDF: 7 pages
Proc. SPIE 7820, International Conference on Image Processing and Pattern Recognition in Industrial Engineering, 78201U (20 August 2010); doi: 10.1117/12.866207
Show Author Affiliations
Gaitang Wang, Northwestern Polytechnical Univ. (China)
Ping Li, Liaoning Shihua Univ. (China)


Published in SPIE Proceedings Vol. 7820:
International Conference on Image Processing and Pattern Recognition in Industrial Engineering
Shaofei Wu; Zhengyu Du; Shaofei Wu; Zhengyu Du; Shaofei Wu; Zhengyu Du, Editor(s)

© SPIE. Terms of Use
Back to Top