A Commodity Classification Framework Based on Machine Learning for Analysis of Trade Declaration

Text, voice, images and videos can express some intentions and facts in daily life. By understanding these contents, people can identify and analyze some behaviors. This paper focuses on the commodity trade declaration process and identifies the commodity categories based on text information on cust...

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Bibliographic Details
Published in:Symmetry (Basel) 2021-06, Vol.13 (6), p.964
Main Authors: He, Mingshu, Wang, Xiaojuan, Zou, Chundong, Dai, Bingying, Jin, Lei
Format: Article
Language:English
Subjects:
Accuracy
Artificial neural networks
Automatic classification
Classification
Commodities
Data processing
Datasets
harmonized System
Literature reviews
Machine learning
Recognition
Semantics
symmetrical decision fusion
Tax regulations
Text categorization
text classification
trade declaration
User behavior
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Summary:Text, voice, images and videos can express some intentions and facts in daily life. By understanding these contents, people can identify and analyze some behaviors. This paper focuses on the commodity trade declaration process and identifies the commodity categories based on text information on customs declarations. Although the technology of text recognition is mature in many application fields, there are few studies on the classification and recognition of customs declaration goods. In this paper, we proposed a classification framework based on machine learning (ML) models for commodity trade declaration that reaches a high rate of accuracy. This paper also proposed a symmetrical decision fusion method for this task based on convolutional neural network (CNN) and transformer. The experimental results show that the fusion model can make up for the shortcomings of the two original models and some improvements have been made. In the two datasets used in this paper, the accuracy can reach 88% and 99%, respectively. To promote the development of study of customs declaration business and Chinese text recognition, we also exposed the proprietary datasets used in this study.
ISSN:2073-8994
2073-8994
DOI:10.3390/sym13060964