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中国精品科技期刊2020
蒋诗泉, 刘中侠, 蒋诗平, 周兴才. 随机森林算法在红葡萄酒质量评价指标体系选择中的应用[J]. 食品工业科技, 2014, (07): 264-267. DOI: 10.13386/j.issn1002-0306.2014.07.059
引用本文: 蒋诗泉, 刘中侠, 蒋诗平, 周兴才. 随机森林算法在红葡萄酒质量评价指标体系选择中的应用[J]. 食品工业科技, 2014, (07): 264-267. DOI: 10.13386/j.issn1002-0306.2014.07.059
JIANG Shi-quan, LIU Zhong-xia, JIANG Shi-ping, ZHOU Xing-cai. Application of random forest algorithm on selecting evaluation index system of the quality of red wine[J]. Science and Technology of Food Industry, 2014, (07): 264-267. DOI: 10.13386/j.issn1002-0306.2014.07.059
Citation: JIANG Shi-quan, LIU Zhong-xia, JIANG Shi-ping, ZHOU Xing-cai. Application of random forest algorithm on selecting evaluation index system of the quality of red wine[J]. Science and Technology of Food Industry, 2014, (07): 264-267. DOI: 10.13386/j.issn1002-0306.2014.07.059

随机森林算法在红葡萄酒质量评价指标体系选择中的应用

Application of random forest algorithm on selecting evaluation index system of the quality of red wine

  • 摘要: 评价指标体系的确定是葡萄酒质量评估的一个关键环节,而指标体系选取的好坏直接影响模型的预测精度。本文利用机器学习的方法———随机森林算法来选择评价指标。仿真实验表明,该算法所确定的指标对葡萄酒的评价更加准确,从而能够有效的减少因品酒师人为因素带来的评价不稳定性。 

     

    Abstract: The determination of evaluation index system was a key link in wine quality evaluation, and it will directly affect the prediction accuracy of the model.A machine learning method--the random forest algorithm was applied to select the evaluation index in this paper.Simulation experiments showed that the evaluation on the wine became more accurate and more objective by using random forest.Thus, it can effectively reduce instability of evaluation for human factors.

     

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