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数据分析与知识发现  2019, Vol. 3 Issue (1): 72-84     https://doi.org/10.11925/infotech.2096-3467.2018.0506
  研究论文 本期目录 | 过刊浏览 | 高级检索 |
中国海关HS编码风险的识别研究*
张紫玄1,2,王昊1,2(),朱立平1,2,3,邓三鸿1,2
1南京大学信息管理学院 南京 210023
2江苏省数据工程与知识服务重点实验室 南京 210023
3中华人民共和国南京海关 南京 210001
Identifying Risks of HS Codes by China Customs
Zixuan Zhang1,2,Hao Wang1,2(),Liping Zhu1,2,3,Sanhong eng1,2
1School of Information Management, Nanjing University, Nanjing 210023, China
2Jiangsu Key Laboratory of Data Engineering and Knowledge Service, Nanjing 210023, China
3Nanjing Customs District, P.R.China, Nanjing 210001, China
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摘要 

【目的】利用HS编码数据中所蕴含的规律, 为海关税收风险判断分析提供有效的知识服务。【方法】提出直接以HS编码作为风险判别目标和以HS编码正误作为风险判别目标两种基于机器学习的自动分类方案解决HS编码风险判断问题, 针对编码目标的结构、特征的性质、文本的长短等特征构建与方案对应的SVM预测模型并进行相应实验。【结果】对以HS编码作为判别目标和以HS编码正误作为判别目标两种预测海关报关风险方案进行探讨与分析, 发现后者对训练数据的要求更低, 预测速度更快, 风险的识别效果也更好。【局限】仅获得4个月的数据, 可能存在样本代表性不足的问题。【结论】最终经过测试获得风险预测率较高的分类器, 为形成可实用的分类模型和判别系统提供了良好的知识基础。

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张紫玄
王昊
朱立平
邓三鸿
关键词 风险识别HS编码预测SVM算法文本分类机器学习    
Abstract

[Objective] This study tries to utilize patterns from the HS codes to provide effective knowledge service for the China customs taxation. [Methods] We proposed two machine learning-based automatic classification schemes. The first one directly used original HS codes as risk identifiers while the other one relied on the correctness of the HS codes. We also built a SVM prediction model and examined the two schemes from the perspectives of target structures and features, as well as the text length. [Results] We found that the second model required less training efforts and processing time and then reached better accuracy. [Limitations] Only used four-month-data to train the new models. [Conclusions] This study finds an effective way to forecast customs risks, and indicate directions of applicable products.

Key wordsRisk Identification    HS Prediction    SVM    Text Classification    Machine Learning
收稿日期: 2018-05-07      出版日期: 2019-03-04
基金资助:*本文系江苏省研究生科研与实践创新计划项目“大数据环境下海关商品归类风险分析和规避研究”(项目编号: SJCX18_0009)和“南京海关税收大数据分析咨询项目”的研究成果之一
引用本文:   
张紫玄,王昊,朱立平,邓三鸿. 中国海关HS编码风险的识别研究*[J]. 数据分析与知识发现, 2019, 3(1): 72-84.
Zixuan Zhang,Hao Wang,Liping Zhu,Sanhong eng. Identifying Risks of HS Codes by China Customs. Data Analysis and Knowledge Discovery, 2019, 3(1): 72-84.
链接本文:  
https://manu44.magtech.com.cn/Jwk_infotech_wk3/CN/10.11925/infotech.2096-3467.2018.0506      或      https://manu44.magtech.com.cn/Jwk_infotech_wk3/CN/Y2019/V3/I1/72
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