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

DOI

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.

Copyright

© 2010, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - JOUR
AU  - Xiaochun Cheng
AU  - Liang Hu
AU  - Xilong Che
PY  - 2010
DA  - 2010/04/01
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  - 10.2991/ijcis.2010.3.1.7
ID  - Cheng2010
ER  -