激光与光电子学进展, 2018, 55 (1): 013006, 网络出版: 2018-09-10
改进的修剪随机森林算法在烟叶近红外光谱产地识别中的应用研究 下载: 1054次
Application of Improved Random Forest Pruning Algorithm in Tobacco Origin Identification of Near Infrared Spectrum
近红外光谱 分类 自适应遗传算法 修剪随机森林 高维数据 near infrared spectrum classification adaptive genetic algorithm random forest pruning high-dimensional data
摘要
为了建立更准确、高效的烟叶产地识别模型,提出了基于自适应遗传算法的修剪随机森林算法(AGARFP)。该算法根据种群的进化程度,适配不同的选择算子;然后利用改进的自适应遗传算法对随机森林进行修剪。实验选择5个产区的样本构建烟叶产地识别模型,以产地识别准确率作为算法优劣的衡量标准。实验结果表明,AGARFP分类准确率为94.67%,分类效果优于其他方法,从而证明了所提算法的有效性。
Abstract
In order to establish a more accurate and efficient identification model of tobacco origin, a random forest pruning algorithm based on adaptive genetic algorithm (AGARFP) is proposed. According to evolution degree of groups, the proposed algorithm can adapt to different selection operators; then, by utilizing the improved adaptive genetic algorithm, random forest pruning can be conducted. The samples of five producing areas are selected to build an identification model for tobacco origin, the precision of origin identification is used as the standard to weigh the pros and cons of the algorithm. Experimental results show that the classification precision of AGARFP can be as high as 94.67%, the classification effects of AGARFP are superior to that of the comparative methods, thus the effectiveness of the proposed algorithm is demonstrated.
孔清清, 丁香乾, 宫会丽. 改进的修剪随机森林算法在烟叶近红外光谱产地识别中的应用研究[J]. 激光与光电子学进展, 2018, 55(1): 013006. 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): 013006.