Constrained Voting Extreme Learning Machine and its Application

Extreme learning machine (ELM) has been proved to be an effective pattern classification and regression learning mechanism by researchers. However, its good performance is based on a large number of hidden layer nodes. In this paper, we propose a novel algorithm, named extreme learning machine (ELM). Compared with the Machine Learning algorithms, the ELM determines the input weight and bias based on the differences of between-class samples. The proposed method is evaluated on public benchmark datasets. The experimental results show that the proposed algorithm is superior to the other Machine Learning models. Further, we apply the ELM to the classification of superheat degree (SD) state in the aluminium electrolysis industry, and the recognition accuracy rate reaches 87.4%, and the experimental results demonstrate that the proposed method is more robust than the methods.

 KEYWORDS: extreme learning machine (ELM), Machine Learning, ensemble method, sample based learning, superheat degree (SD).