Volume 3, Issue 1, April 2010, Pages 70 - 83
Bandwidth Prediction based on Nu-Support Vector Regression and Parallel Hybrid Particle Swarm Optimization
Xiaochun Cheng, Liang Hu, Xilong Che
Received 18 December 2008, Accepted 27 November 2009, Available Online 1 April 2010.
- https://doi.org/10.2991/ijcis.2010.3.1.7How to use a DOI?
- bandwidth prediction; hyper-parameter selection; feature selection; nu-support vector regression; parallel hybrid particle swarm optimization
- 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.
- Open Access
- This is an open access article distributed under the CC BY-NC license.
Cite this article
TY - JOUR AU - Xiaochun Cheng AU - Liang Hu AU - Xilong Che PY - 2010 DA - 2010/04 TI - Bandwidth Prediction based on Nu-Support Vector Regression and Parallel Hybrid Particle Swarm Optimization JO - International Journal of Computational Intelligence Systems SP - 70 EP - 83 VL - 3 IS - 1 SN - 1875-6883 UR - https://doi.org/10.2991/ijcis.2010.3.1.7 DO - https://doi.org/10.2991/ijcis.2010.3.1.7 ID - Cheng2010 ER -