An improved PSO algorithm coupling with prior information for classification of large scale dataset

DOI

10.2991/icecee-15.2015.263How to use a DOI?

Keywords

Particle Swarm Optimization; Prior Information; Classification

Abstract

An improved particle swarm optimization (PSO) algorithm coupling with prior information for classification of large scale dataset is proposed in this paper. The prior information derived from the data set is used to determine the initial position of the particles. In the new algorithm, neural network is first trained by improved PSO and then by back-propagation (BP). The prior information narrows the search space and guides the movement direction of the particles, so the convergence rate and the generalization performance are improved. Experimental results demonstrate that the new algorithm is more effective than traditional methods.

Copyright

© 2015, 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  - CONF
AU  - Juanjuan Tu
AU  - Wenlan Zhou
AU  - Hongmei Li
PY  - 2015/06
DA  - 2015/06
TI  - An improved PSO algorithm coupling with prior information for classification of large scale dataset
BT  - Proceedings of the 2015 International Conference on Electrical, Computer Engineering and Electronics
PB  - Atlantis Press
SP  - 1409
EP  - 1414
SN  - 2352-538X
UR  - https://doi.org/10.2991/icecee-15.2015.263
DO  - 10.2991/icecee-15.2015.263
ID  - Tu2015/06
ER  -