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支持向量机决策树在隐患预警模型中的应用
引用本文:闫晓静,于放,孙咏,肖卡飞,王嵩.支持向量机决策树在隐患预警模型中的应用[J].计算机系统应用,2017,26(2):212-216.
作者姓名:闫晓静  于放  孙咏  肖卡飞  王嵩
作者单位:中国科学院大学, 北京 100049;中国科学院 沈阳计算技术研究所, 沈阳 110168,中国科学院 沈阳计算技术研究所, 沈阳 110168,中国科学院 沈阳计算技术研究所, 沈阳 110168,中国科学院大学, 北京 100049;中国科学院 沈阳计算技术研究所, 沈阳 110168,中国科学院 沈阳计算技术研究所, 沈阳 110168
摘    要:危化企业的安全监控数据具有社会价值,对安全隐患进行实时精确的预测是预警研究的热点,本文从人、设备、环境和管理四个维度出发,对安全生产隐患预警的相关指标进行分析,构建隐患预警指标体系,在此基础上,构建了自底向上的基于支持向量机的决策树多分类预警模型,实现对安全等级的的准确分类并用于预警未来的安全生产状态,通过与自顶向下的多分类模型比较,证实本文所采用的预警模型具有较好的实时性和精确度,满足对预警模型的基本要求.

关 键 词:预警模型  支持向量机  决策树
收稿时间:2016/5/16 0:00:00
修稿时间:2016/6/16 0:00:00

Risk Early-Warning Model Based on SVM Decision Tree
YAN Xiao-Jing,YU Fang,SUN Yong,XIAO Ka-Fei and WANG Song.Risk Early-Warning Model Based on SVM Decision Tree[J].Computer Systems& Applications,2017,26(2):212-216.
Authors:YAN Xiao-Jing  YU Fang  SUN Yong  XIAO Ka-Fei and WANG Song
Affiliation:University of Chinese Academy of Sciences, Beijing 100049, China;Shenyang Institute of Computing Technology, Chinese Academy of Sciences, Shenyang 110168, China,Shenyang Institute of Computing Technology, Chinese Academy of Sciences, Shenyang 110168, China,Shenyang Institute of Computing Technology, Chinese Academy of Sciences, Shenyang 110168, China,University of Chinese Academy of Sciences, Beijing 100049, China;Shenyang Institute of Computing Technology, Chinese Academy of Sciences, Shenyang 110168, China and Shenyang Institute of Computing Technology, Chinese Academy of Sciences, Shenyang 110168, China
Abstract:The security monitoring data of Dangerous chemicals business has great social value, especially real-time accurate prediction of the security risk has become a hot warning research. From the view of four dimensions which are people, equipment, the environment and management, this article analyzes the relevant indicators of safety hazards warning, constructs the bottom-up decision tree based on multi-classification SVM warning model, constructs a bottom-up decision tree SVM multi-classification model based on early warning, to achieve the security level of accurate classification and for future production safety status warning. By comparison with more top-down classification model, it confirms that early warning model used in this paper has better performance in real-time and accuracy, and meets the basic requirements of early warning models.
Keywords:early-warning model  SVM  decision tree
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