Forecasting Model of Mass Incidents in China
Purpose Mass incidents have emerged as a serious social problem concerning national security in China. So, it is necessary to construct a forecasting model to predict such public events. In this paper, Support Vector Machines are applied to the model. Method Based on the social surveys conducted in 119 counties of Shanxi, Gansu and Hubei provinces, 3 multi-class classification problems were proposed, and then 3 multi-class Support Vector Classification forecasting models were constructed. Results Preliminary experiments have proved that our method, compared with multiple cumulative logistic regression, should be more effective and accurate(enter method as well as the stepwise one). Conclusion It can be concluded from the results that irrationally behavioral intentions can be predicted more accurate than those rational ones. When the collective attitudes are applied to the forecast of the collective behavioral intentions, SVM method was approved to be the most effective approach. This paper represents an originally explorative research.
Mass incident Collective action Classification Support Vector Machine Forecasting Model
Jiashu Zhou Erping Wang Yiwen Chen Xuanna Wu Yujie Ma Yingjie Tian
Institute of Psychology, Chinese Academy of Sciences, Beijing, 100101, P.R.China Graduate University Institute of Psychology, Chinese Academy of Sciences, Beijing, 100101, P.R.China Institute of Mathematics, AMSS, Chinese Academy of Sciences, Beijing, 100190, P.R.China Research Center of Fictitious Economy and Data Science, Chinese Academy of Sciences Beijing, 100190,
国际会议
北京
英文
152-155
2009-07-24（万方平台首次上网日期，不代表论文的发表时间）