Affiliations: [a] School of Information Engineering, Shandong Management University, Jinan, China
| [b] College of Computer and Information Engineering, Henan Normal University, Xinxiang, China
Correspondence:
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Corresponding authors. Di Guo, College of Computer and Information Engineering, Henan Normal University, Xinxiang 453007, China. Tel.: +155 601 21353; E-mail: [email protected] and Ting Zhou, School of Information Engineering, Shandong Management University, Jinan 250357, China. Tel.: +135 250 80958; E-mail: [email protected].
Abstract: Extreme learning machine (ELM) has received increasingly more attention because of its high efficiency and ease of implementation. However, the existing ELM algorithms generally suffer from the drawbacks of noise sensitivity and poor robustness. Therefore, we combine the advantages of twin hyperplanes with the fast speed of ELM, and then introduce the characteristics of heteroscedastic Gaussian noise. In this paper, a new regressor is proposed, which is called twin extreme learning machine based on heteroskedastic Gaussian noise (TELM-HGN). In addition, the augmented Lagrange multiplier method is introduced to optimize and solve the presented model. Finally, a significant number of experiments were conducted on different data-sets including real wind-speed data, Boston housing price dataset and stock dataset. Experimental results show that the proposed algorithms not only inherits most of the merits of the original ELM, but also has more stable and reliable generalization performance and more accurate prediction results. These applications demonstrate the correctness and effectiveness of the proposed model.
Keywords: Extreme learning machine, heteroscedastic Gaussian noise, least squares support vector regression, twin hyperplanes, wind-speed forecasting
