Chinese Text Classification Based on Ant Colony Optimization

DOI

10.2991/nceece-15.2016.8How to use a DOI?

Keywords

Text processing; classification; Artificial intelligence; Ant colony optimization;

Abstract

It's significance for us to study Chinese Text Classification, when we face so much dynamic information. The development of Text Classification has a close connection with Pattern Recognition. However, some peculiarity of Chinese Text Classification, such as it has many classes, much noise, and excessive samples, make Pattern Recognition difficult to classify texts. Recently, Artificial Intelligence provides a new intellectualized method to Text Classification. This paper tentatively leads Ant Colony Optimization, a ripe algorithm of Swarm Intelligence, into Text Classification. We construct a Text ACO-Miner Classification Model based on Ant Colony Optimization, and test it. The result shows the model can accurately be used to classify Chinese texts.

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/).

Download article (PDF)

TY  - CONF
AU  - Xin Luo
PY  - 2015/12
DA  - 2015/12
TI  - Chinese Text Classification Based on Ant Colony Optimization
BT  - Proceedings of the 2015 4th National Conference on Electrical, Electronics and Computer Engineering
PB  - Atlantis Press
SP  - 37
EP  - 41
SN  - 2352-5401
UR  - https://doi.org/10.2991/nceece-15.2016.8
DO  - 10.2991/nceece-15.2016.8
ID  - Luo2015/12
ER  -