基于Probit的犯罪嫌疑人判定方法研究

为了在涉案人群中实现计算机识别犯罪嫌疑人,协助办案并提高办案效率,提出了一种基于Probit模型的犯罪嫌疑人判定技术.采用聚类的分离算法、关联算法以及Probit模型的显著性水平参数发现重要属性,通过对重要属性提取后的数据进行训练得到犯罪风险判定模型.实验结果表明,该方法对嫌疑人判定的平均准确率达到90.5%,平均查全率达到92.7%,判定效果较好.

Research on Suspected Culprit Recognition Based on Probit

In order to recognize the criminal suspects in the crowd relevant to the case by computer and improve the efficiency of solving the case, this paper presents a new method of suspected culprit recognition based on Probit model. Separation algorithm, correlation algorithm based on cluster and the parameters named significance level in Probit model are adopted to find the important attributes of the culprit, and then, by training the data just including the important attributes, the crime risk evaluation model could be achieved. The experimental results show that average accuracy of model-recognizing-culprit is 90.5% and the average recall is 92.7%.