Bandwidth prediction based on nu-support vector regression and parallel hybrid particle swarm optimization
Article
Cheng, X., Che, X. and Hu, L. 2010. Bandwidth prediction based on nu-support vector regression and parallel hybrid particle swarm optimization. International Journal of Computational Intelligence Systems. 3 (1), pp. 70-83. https://doi.org/10.2991/ijcis.2010.3.1.7
| Type | Article |
|---|---|
| Title | Bandwidth prediction based on nu-support vector regression and parallel hybrid particle swarm optimization |
| Authors | Cheng, X., Che, X. and Hu, L. |
| Abstract | This paper addresses the problem of generating multi-step-ahead bandwidth prediction. Variation of bandwidth is modeled as a Nu-Support Vector Regression (Nu-SVR) procedure. A parallel procedure is proposed to hybridize constant and binary Particle Swarm Optimization (PSO) together for optimizing feature selection and hyper-parameter selection. Experimental results on benchmark data set show that the Nu-SVR model optimized achieves better accuracy than BP neural network and SVR without optimization. As a combination of feature selection and hyper-parameter selection, parallel hybrid PSO achieves better convergence performance than individual ones, and it can improve the accuracy of prediction model efficiently. |
| Keywords | bandwidth prediction; hyper-parameter selection; feature selection; nu-support vector regression; parallel hybrid particle swarm optimization |
| Research Group | Artificial Intelligence group |
| Publisher | Atlantis Press |
| Journal | International Journal of Computational Intelligence Systems |
| ISSN | 1875-6891 |
| Electronic | 1875-6883 |
| Publication dates | |
| Online | 01 Apr 2010 |
| Apr 2010 | |
| Publication process dates | |
| Submitted | 18 Dec 2008 |
| Accepted | 27 Nov 2009 |
| Deposited | 03 Jul 2013 |
| Output status | Published |
| Publisher's version | License File Access Level Open |
| Copyright Statement | Copyright |
| Digital Object Identifier (DOI) | https://doi.org/10.2991/ijcis.2010.3.1.7 |
| Web of Science identifier | WOS:000277350100007 |
| Language | English |
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