International Journal of Computational Intelligence Systems

Volume 3, Issue 1, April 2010, Pages 70 - 83

Bandwidth Prediction based on Nu-Support Vector Regression and Parallel Hybrid Particle Swarm Optimization

Authors
Xiaochun Cheng, Liang Hu, Xilong Che
Corresponding Author
Xilong Che
Received 18 December 2008, Accepted 27 November 2009, Available Online 1 April 2010.
DOI
https://doi.org/10.2991/ijcis.2010.3.1.7How to use a DOI?
Keywords
bandwidth prediction; hyper-parameter selection; feature selection; nu-support vector regression; parallel hybrid particle swarm optimization
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.
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This is an open access article distributed under the CC BY-NC license.

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Journal
International Journal of Computational Intelligence Systems
Volume-Issue
3 - 1
Pages
70 - 83
Publication Date
2010/04
ISSN (Online)
1875-6883
ISSN (Print)
1875-6891
DOI
https://doi.org/10.2991/ijcis.2010.3.1.7How to use a DOI?
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  -