Proceedings of the 2015 4th National Conference on Electrical, Electronics and Computer Engineering

Chinese Text Classification Based on Particle Swarm Optimization

Authors
Xin Luo
Corresponding Author
Xin Luo
Available Online December 2015.
DOI
10.2991/nceece-15.2016.11How to use a DOI?
Keywords
Text processing; Classification; Artificial Intelligence; Particle swarm optimization (PSO)
Abstract

For mass, heterogeneous and dynamic text information, it has important significance to the text classification. In recent years, the theory and method of Swarm Intelligence has been developed gradually, which provides a new method for text classification. In this paper, the intelligent algorithm of swarm intelligence, Particle Swarm Optimization (PSO), is introduced into the field of text classification. A text classification model Text PSO-Miner based on PSO is constructed and tested on the Chinese text set. The results show that Text PSO-Miner can be well applied to Chinese text classification.

Copyright
© 2016, 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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Volume Title
Proceedings of the 2015 4th National Conference on Electrical, Electronics and Computer Engineering
Series
Advances in Engineering Research
Publication Date
December 2015
ISBN
978-94-6252-150-6
ISSN
2352-5401
DOI
10.2991/nceece-15.2016.11How to use a DOI?
Copyright
© 2016, 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/).

Cite this article

TY  - CONF
AU  - Xin Luo
PY  - 2015/12
DA  - 2015/12
TI  - Chinese Text Classification Based on Particle Swarm Optimization
BT  - Proceedings of the 2015 4th National Conference on Electrical, Electronics and Computer Engineering
PB  - Atlantis Press
SP  - 53
EP  - 58
SN  - 2352-5401
UR  - https://doi.org/10.2991/nceece-15.2016.11
DO  - 10.2991/nceece-15.2016.11
ID  - Luo2015/12
ER  -