生态与农村环境学报 ›› 2012, Vol. 28 ›› Issue (2): 217-221.doi:

• 污染控制与修复 • 上一篇    下一篇

基于多类支持向量机的化学物质生态危害分类研究

杨雪梅, 李书琴, 杨会君, 刘济宁   

  1. 西北农林科技大学信息工程学院
  • 收稿日期:2011-10-17 修回日期:2011-12-21 出版日期:2012-03-25 发布日期:2012-03-27
  • 通讯作者: 李书琴 西北农林科技大学信息工程学院 E-mail:lsq_cie@nwsuaf.edu.cn
  • 作者简介:杨雪梅(1986-),女,重庆合川人,硕士生,主要研究方向为智能信息系统。E-mail:yxm861128@163.com
  • 基金资助:

    环保公益性行业科研专项

Classification of Chemicals Ecological Hazard Using Multi-Class Support Vector Machines

YANG  Xue-Mei, LI  Shu-Qin, YANG  Hui-Jun, LIU  Ji-Ning   

  1. College of Information Engineering,Northwest A&F University
  • Received:2011-10-17 Revised:2011-12-21 Online:2012-03-25 Published:2012-03-27
  • Contact: LI Shu-Qin College of Information Engineering,Northwest A&F University E-mail:lsq_cie@nwsuaf.edu.cn

摘要: 应用多类支持向量机(M-SVMs)方法研究了化学物质生态危害程度的分类,以提高分类的准确性和效率。对采集到的61种环境优先污染物的环境行为和生物毒性方面的7项指标进行相关性分析,去除与鱼毒有信息重叠的溞毒,建立了M-SVMs分类模型并对数据集进行10折交叉验证以评价模型的分类能力,运用所建模型对7种化学物质的生态危害进行预测。结果表明,去除与鱼毒有信息重叠的溞毒指标,选取鱼毒、藻毒、降解性、蓄积性、分配系数和吸附系数6项指标用于构建M-SVMs分类模型;M-SVMs模型识别率较高,交叉验证平均分类正确率达86.89%;对7种化学物质生态危害的预测结果与实际情况基本相符。

关键词: 多类支持向量机, 化学物质, 生态危害, 分类

Abstract: Classification of chemicals by ecological hazard was studied with the multi-class support vector machines(M-SVMs)to improve accuracy and efficiency of the classification.A total of 61 environmental priority pollutants that had already been collected were analyzed for correlation between 7 inexes in the aspects of environmental behavior and biotoxicity.On such a basis a M-SVMs classification model was established and 10-fold cross validation of its dataset was conducted to evaluate classification ability of the model.Then the model was applied to predict ecological hazard of 7 chemicals.Results show that the index of daphnia toxicity,overlapping some of the information of the index of fish toxicity,was excluded from the 7 indexes.So only 6,i.e. as fish toxicity,alga toxicity,biodegradability,bioconcentration,distributivity and adsorbability were selected in building the M-SVMs classification model.The M-SVMs model was quite high in identification rate and cross-validation indicated theat its mean classification accuracy reached up to 86.89%,and its prediction of the 7 chemicals in ecological hazard basically tallies with the actual situation.

Key words: multi-call support vector machines(M-SVMs), chemicals, ecological hazard, classification

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