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Research on Genetic Algorithm Based on Mutual Information in the Spectrum Selection |
KONG Qing-qing, GONG Hui-li*, DING Xiang-qian, LIU Ming |
College of Information Science and Engineering, Ocean University of China, Qingdao 266100, China |
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Abstract It is vital to establish an accurate and robust quantitative model in near-infrared spectroscopy. The whole spectrum modeling can increase the computational time of modeling and forecasting, and reduce the robustness and precision. Therefore the effective variable selection method is very important for model construction. To address this problem, this paper proposed a genetic algorithm based on mutual information (GAs-MI) to select features. Mutual information filtered out a large number of unrelated information and redundant information. Genetic algorithm further selected the features with high discernment. Shapley value method was introduced to reduce the randomness of artificial setting parameters in the mutation process of genetic algorithm. In order to validate the validity of the algorithm, 273 representative tobacco samples were selected as the experimental materials. 182 samples were randomly selected to construct the PLS quantitative model of tobacco nicotine,and the remaining samples were used as the test set. The Correlation Coefficient (R), the Root Means Square Error of Cross Validation (RMSECV) and the Root Mean Square Error of Prediction (RMSEP) were used as the model evaluation indexes. The experimental results showed that the model established by the selected wavelength was simpler and more predictive.
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Received: 2017-02-28
Accepted: 2017-06-25
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Corresponding Authors:
GONG Hui-li
E-mail: huiligong@163.com
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[1] CHU Xiao-li, LU Wan-zhen(褚小立,陆婉珍). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2014, 34(10): 2595.
[2] SUN Tong, WU Yi-qing, LI Xiao-zhen, et al(孙 通,吴宜青,李晓珍,等). Acta Optica Sinica(光学学报),2015,35(16):0630005.
[3] ZHANG Long, PAN Jia-rong, ZHU Cheng(张 龙,潘家荣,朱 诚). Food Science(食品科学), 2013, 34(6): 167.
[4] SONG Sha-lei, LI Ping-xiang, GONG Wei, et al(宋沙磊,李平湘,龚 威,等). Geomatics and Information Science of Wuhan University(武汉大学学报·信息科学版),2010,35(2):219.
[5] SHI Ji-yong, ZOU Xiao-bo, ZHAO Jie-wen, et al(石吉勇,邹小波,赵杰文,等). Journal of Infrared and Millimeter Waves(红外与毫米学报), 2011, 30(5): 458.
[6] CHENG Biao, CHEN De-zhao, WU Xiao-hua(成 飙,陈德钊,吴晓华). Chinese Journal of Analytical Chemistry(分析化学),2006,34(9):123.
[7] ZHU Shi-ping, WANG Yi-ming, ZHANG Xiao-chao, et al(祝诗平,王一鸣,张小超,等). Transactions of The Chinese Society of Agricultural Machinery(农业机械学报), 2004, 35(5): 152.
[8] ZOU Xiao-bo, ZHAO Jie-wen(邹小波,赵杰文). Acta Optica Sinica(光学学报), 2007, 27(7): 1316.
[9] TANG Shi-wei, LIU Xian-mei(唐世伟,刘贤梅). Information Theory(信息论). Harbin: Harbin Engineering University Press(哈尔滨: 哈尔滨工业大学出版社), 2009.
[10] FAN Xue-li, FENG Hai-hong, YUAN Meng(范雪莉,冯海泓,原 猛). Control and Decision(控制与决策), 2013, 28(6): 915.
[11] Bezalel Peleg, Peter Sudholter. Introduction on the Theory of Cooperative Games 2nd ed. Springer, 2007.
[12] Mojtaba Sadegh, Najmeh Mahjouri, Reze Kerachian. Water Resour Manage, 2010, 24(10): 2291. |
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