多种分类模型在海关智能化风险布控中的应用

Application of Multiple Classification Models in Intelligent Customs Risk Prevention and Control

海关监管部门在风险布控的过程中,需要风险分析人员依据经验对各种商品的风险进行人工分类。本文用Logistic回归模型、决策树模型、随机森林模型等几种的分类模型优化风险布控过程,通过将报关数据输入分类模型得到特定商品的风险评估结果,辅助风险分析人员做出正确判断。通过实验验证这种智能化的方法能够有效克服人工风险布控中的不足,完成智能化风险布控,进一步维护国门口岸安全。

As part of the process of risk prevention and control,the customs supervision department requires risk analysts to manually classify risks of various commodities based on their experiences of risk management.This paper uses several classification models,such as logistic model,decision tree model,and random forest model,to optimize risk control process.Risk assessment results of specific commodities can be obtained by inputting customs declaration data into the classification models.Thus the results assist risk analysts to make correct judgments.The proposed model is verified through experiments.The results show that it is an intelligent method and can effectively overcome the shortcomings in manual risk control,complete the intelligent risk control,and further maintain the security of national ports.