管理评论

›› 2016, Vol. 28 ›› Issue (11): 245-251.

• 应急管理 • 上一篇    下一篇

基于支持向量机的治安高危人员风险预警方法

张强1,2, 何乐平1   

  1. 1. 中国科学院大学经济与管理学院, 北京 100190;
    2. 江西省吉安市人民政府, 吉安 343000
  • 收稿日期:2016-05-09 出版日期:2016-11-28 发布日期:2016-11-23
  • 作者简介:张强,中国科学院大学经济与管理学院博士研究生;何乐平,中国科学院大学经济与管理学院博士研究生。

A Risk Early Warning Method for High-risk Groups of Social Security Based on Support Vector Machine

Zhang Qiang1,2, He Leping1   

  1. 1. School of Economics and Management, University of Chinese Academy of Sciences, Beijing 100190;
    2. The People's Government of Ji'an, Ji'an 343000
  • Received:2016-05-09 Online:2016-11-28 Published:2016-11-23

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摘要：

随着社会经济快速发展，城市人口流动性增加，社会治安面临新的挑战。提高对治安高危人员风险预警能力，有助于公安机关高效应对复杂多变的公共安全形势。本文采用支持向量机构建风险预警模型，探求高危人员风险预警的可行方法，并进行实证分析。结果显示该方法对治安高危人员风险预警效果显著，对公安机关的情报研判具有较高的实用性。

关键词: 治安高危人员, 公共安全, 风险预警, 支持向量机

Abstract:

With the rapid development of economy and mobility of population, public security has been facing new challenges. To timely deal with the complicated and changing risk of public security, a key step is to improve the ability of risk early warning of potentially high-risk groups. In this paper we use support vector machine (SVM) algorithm to build a risk early warning model, which shows a cer-tain guiding significance to an effective early-warning on high-risk groups.

Key words: high-risk groups, public security, risk early warning, support vector machine