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
[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.
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