Screening the Effective Spectrum Features of Tobacco Leaf Based on GA and SVM

DOI

10.2991/icsnce-16.2016.40How to use a DOI?

Keywords

Genetic algorithm; Support vector machine; Tobacco grade; Spectrum

Abstract

To improve the tobacco classification speed, it is necessary to shorten the data acquisition time and reduce the computational complexity of the hierarchy model. In this paper, we take the genetic algorithm to screen the tobacco spectrum characteristics, and set up the support vector machine (SVM) classification mode, then compared the feature selection recognition rate of 13 tobacco leaves grade before and after. The experiment results show that the recognition rate improves greatly after using genetic algorithm for feature selection, and reduce the data acquisition quantity. By using the genetic algorithm method, we can improve the classification speed of tobacco leaves grading on the premise of the correct classification rate.

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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TY  - CONF
AU  - Hang Li
AU  - Jinyuan Shen
AU  - Yinliang Kong
AU  - Zhongji Cheng
PY  - 2016/07
DA  - 2016/07
TI  - Screening the Effective Spectrum Features of Tobacco Leaf Based on GA and SVM
BT  - Proceedings of the 2016 International Conference on Sensor Network and Computer Engineering
PB  - Atlantis Press
SP  - 201
EP  - 204
SN  - 2352-5401
UR  - https://doi.org/10.2991/icsnce-16.2016.40
DO  - 10.2991/icsnce-16.2016.40
ID  - Li2016/07
ER  -