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中国农学通报 ›› 2015, Vol. 31 ›› Issue (19): 232-236.doi: 10.11924/j.issn.1000-6850.casb15010019

所属专题: 食用菌 食用菌

• 食品 营养 检测 安全 • 上一篇    下一篇

基于支持向量机的蘑菇毒性判别研究

樊哿,彭卫,孙山,刘峻呈   

  1. 四川农业大学商学院,四川农业大学商学院,湖南商学院经济与贸易学院,湖南中医药大学中医学院
  • 收稿日期:2015-01-05 修回日期:2015-04-30 接受日期:2015-05-14 出版日期:2015-07-28 发布日期:2015-07-28
  • 通讯作者: 彭卫
  • 基金资助:
    四川省教育厅基金项目 (13ZB0287)、四川农业大学科研兴趣项目(2014296)

Discriminant method of toxicity of mushroom based on support vector machine

樊哿,, and   

  • Received:2015-01-05 Revised:2015-04-30 Accepted:2015-05-14 Online:2015-07-28 Published:2015-07-28

摘要: 毒蘑菇和可食用蘑菇在外表上非常相似,依靠传统方法难以判别。为了实现判别上的自动化和增强可靠性,提出了一种基于支持向量机的蘑菇毒性判别方法。首先给出了数据样本和数据预处理的方法,其次建立C-SVM模型并进行训练,同时依照一对一方法实现了支持向量机的多分类,最后使用定步长探索法获得了模型的最优参数。仿真实验对比分析了不同样本量,不同参数下所提方法的准确度,验证了该方法在蘑菇毒性判别上的可行性。同时,使用神经网络、决策树方法进行分类器间的性能对比,发现与神经网络、决策树的判别结果相比,所提方法具有准确率高、操作方便、实用性强等优点。

关键词: 花生, 花生, 育种, 发展历程, 育种技术, 育种目标

Abstract: The resemblance between edible mushroom and poisonous mushroom in appearance makes it hard to distinguish them from each other by conventional methods. In order to achieve the automation of judgment and strengthen the reliability, this paper proposed a method to measure the toxicity of mushroom based on support vector machine. To begin with, collection and pre-processing of the sample data were conducted. Then C-SVM model was built up and trained in accordance with one-to-one principle to further achieve multiclassification by support vector machine. At last, constant step length method was applied to obtain the optimum parameters of the model. By comparing accuracy of SVM classification in diverse sample sizes and parameters, the feasibility was verified in simulation experiments. SVM was more accurate, easy-conducting and practical comparing with neural network and decision tree.