Multi-class AdaBoost ELM

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. As verified by the simulation results, ELM tends to have better scalability and can achieve much better generalization performance and much faster learning speed comparing with traditional SVM. In this paper, we introduce a Multi-class 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 existed boosting ELM algorithm, our algorithm can be directly used in multi-class 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.