• Laser & Optoelectronics Progress
  • Vol. 55, Issue 1, 13006 (2018)
Kong Qingqing, Ding Xiangqian, and Gong Huili*
Author Affiliations
  • College of Information Science and Engineering, Ocean University of China, Qingdao, Shandong 266100, China
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    DOI: 10.3788/LOP55.013006 Cite this Article Set citation alerts
    Kong Qingqing, Ding Xiangqian, Gong Huili. Application of Improved Random Forest Pruning Algorithm in Tobacco Origin Identification of Near Infrared Spectrum[J]. Laser & Optoelectronics Progress, 2018, 55(1): 13006 Copy Citation Text show less
    References

    [1] Guo Z M, Chen L P, Huang W Q et al. Application of genetic algorithm-least squares support vector regression with near infrared spectroscopy for prediction of nicotine content in tobacco[J]. Laser & Optoelectronics Progress, 49, 021201(2012).

    [2] Zhang Y J, Xu X X, Song N et al. The evaluation of hydrocarbon potential generation for source rocks by near-infrared diffuse reflection spectra[J]. Spectroscopy and Spectral Analysis, 31, 955-959(2011).

    [3] Zhang C, Liu F, Kong W W et al. Fast identification of watermelon seed variety using near infrared hyperspectral imaging technology[J]. Transactions of the Chinese Society of Agricultural Engineering, 29, 270-277(2013).

    [4] Yuan M Y, Huang B S, Yu C et al. A NIR qualitative and quantitative model of 8 kinds of carbonate-containing mineral Chinese medicines[J]. China Journal of Chinese Materia Medica, 39, 267-272(2014).

    [5] Zhao J W, Bi X K, Lin H et al. Visible-near-infrared transmission spectra for rapid analysis of the freshness of eggs[J]. Laser & Optoelectronics Progress, 50, 053003(2013).

    [6] Hana M, Mcclure W, Whitaker T et al. Applying artificial neural networks: Part Ⅱ. using near infrared data to classify tobacco types and identify native grown tobacco[J]. Journal of Near Infrared Spectroscopy, 5, 19-25(1997). http://adsabs.harvard.edu/abs/1997JNIS....5...19H

    [7] Shu R X, Sun P, Yang K et al[J]. NIR-PCA-SVM based pattern recognition of growing area of flue-cured tobacco Tobacco Science & Technology, 2011, 51-52.

    [8] Shi F C, Li D L, Feng G L et al[J]. Discrimination of producing areas of flue-cured tobacco leaves with near infrared spectroscopy-based PLS-DA algorithm Tobacco Science & Technology, 2013, 56-59.

    [9] Jiang B, Luo A L, Zhao Y H. Datamining approach to cataclysmic variables candidates based on random forest algorithm[J]. Spectroscopy and Spectral Analysis, 32, 510-513(2012).

    [10] Li X H. Using "random forest" for classification and regression[J]. Chinese Journal of Applied Entomology, 50, 1190-1197(2013).

    [11] Li T, Ni B B, Wu X Y et al. On random hyper-class random forest for visual classification[J]. Neurocomputing, 172, 281-289(2016). http://www.sciencedirect.com/science/article/pii/S0925231215005901

    [12] Yang F, Lu W H, Luo L K et al. Margin optimization based pruning for random forest[J]. Neurocomputing, 94, 54-63(2012). http://www.sciencedirect.com/science/article/pii/S0925231212003396

    [13] Xu Y G, Zhang J Y, Gong X G et al. A method of real-time traffic classification in secure access of the power enterprise based on improved random forest algorithm[J]. Power System Protection and Control, 44, 82-89(2016).

    [14] Qiu Y H. Customer-churn prediction for telecom enterprises based on pruning random forest[J]. Journal of Xiamen University (Natural Science), 53, 817-823(2014).

    [15] Liu W Y, Liu B. Adaptive genetic algorithm based on co-evolution[J]. Computer Engineering and Applications, 47, 31-36(2011).

    [16] Duan Y M, Xiao H H. Improved fruit fly algorithm for TSP problem[J]. Computer Engineering and Applications, 52, 144-149(2016).

    [17] Yu Y Y, Chen Y, Li T Y. Improved genetic algorithm for solving TSP[J]. Control and Decision, 29, 1483-1488(2014).

    Kong Qingqing, Ding Xiangqian, Gong Huili. Application of Improved Random Forest Pruning Algorithm in Tobacco Origin Identification of Near Infrared Spectrum[J]. Laser & Optoelectronics Progress, 2018, 55(1): 13006
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