• 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
    Flow chart of AGARFP
    Fig. 1. Flow chart of AGARFP
    Evolution curves of fitness for genetic algorithms with different selection operators. (a) Roulette method selection operator; (b) tournament method selection operator; (c) proposed adaptive selection operator
    Fig. 2. Evolution curves of fitness for genetic algorithms with different selection operators. (a) Roulette method selection operator; (b) tournament method selection operator; (c) proposed adaptive selection operator
    Relationship between random forest size and classification accuracy
    Fig. 3. Relationship between random forest size and classification accuracy
    Classification effect diagrams of RF and AGARFP. (a) RF; (b) AGARFP
    Fig. 4. Classification effect diagrams of RF and AGARFP. (a) RF; (b) AGARFP
    SelectionoperatorConvergencerate /generationOptimumsolution
    Roulette method93.270.8800
    Tournament method72.150.8267
    Proposed operator79.360.9467
    Table 1. Comparison of different selection operators
    Classification algorithmCARTRFAGARFPSVMPLS-DASIMCA
    Accuracy /%73.3392.0094.6776.0090.6789.33
    Table 2. Comparison of different classification algorithms
    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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