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Mathematical Problems in Engineering
Volume 2015, Article ID 918105, 9 pages
Research Article

Multiclass AdaBoost ELM and Its Application in LBP Based Face Recognition

1Institute of Cyber-Systems and Control, Zhejiang University, Hangzhou 310027, China
2School of Information & Engineering, Huzhou Teachers College, Huzhou 313000, China
3School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou 310018, China

Received 22 August 2014; Revised 11 November 2014; Accepted 18 November 2014

Academic Editor: Jiuwen Cao

Copyright © 2015 Yunliang Jiang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Extreme learning machine (ELM) is a competitive machine learning technique, which is simple in theory and fast in implementation; it can identify faults quickly and precisely as compared with traditional identification techniques such as support vector machines (SVM). As verified by the simulation results, ELM tends to have better scalability and can achieve much better generalization performance and much faster learning speed compared with traditional SVM. In this paper, we introduce a multiclass AdaBoost based ELM ensemble method. In our approach, the ELM algorithm is selected as the basic ensemble predictor due to its rapid speed and good performance. Compared with the existing boosting ELM algorithm, our algorithm can be directly used in multiclass classification problem. We also carried out comparable experiments with face recognition datasets. The experimental results show that the proposed algorithm can not only make the predicting result more stable, but also achieve better generalization performance.