A Study on the Classification of Dongba Literature

DOI

10.2991/isaeece-16.2016.38How to use a DOI?

Keywords

Classification,Dongba literature,Mutual information,Literature feature,SVM

Abstract

With dongba culture researches increasing year by year, there needs to be a highly efficient classification method to classify research achievements creating conditions for further study. Aiming at the shortcomings of the traditional mutual information method, giving full consideration to the factors such as word frequency, concentration and dispersion, and using the difference between the maximum and the second large value as a global evaluation function, GMI feature selection algorithm is proposed. Use this algorithm to choose text feature after one dimension reduction, and then get classification feature combined with the literature feature on secondary dimension reduction,and finally utilize the SVM to classify dongba literature. The experimental results show that the average accuracy rate and recall rate in all categories are 83% and 82% respectively. Experimental results show the proposed method is feasible in the dongba literature 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/).

Download article (PDF)

TY  - CONF
AU  - Yujing Chen
AU  - Ning Li
AU  - Xueqiang Lv
PY  - 2016/04
DA  - 2016/04
TI  - A Study on the Classification of Dongba Literature
BT  - Proceedings of the 2016 International Symposium on Advances in Electrical, Electronics and Computer Engineering
PB  - Atlantis Press
SP  - 196
EP  - 199
SN  - 2352-5401
UR  - https://doi.org/10.2991/isaeece-16.2016.38
DO  - 10.2991/isaeece-16.2016.38
ID  - Chen2016/04
ER  -