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

• Main Authors: Mingshu He, Xiaojuan Wang, Chundong Zou, Bingying Dai, Lei Jin
• Format: Article
• Language: English
• Published: MDPI AG 2021-05-01
• Series: Symmetry
• Subjects: trade declaration; machine learning; text classification; symmetrical decision fusion; harmonized System
• Online Access: https://www.mdpi.com/2073-8994/13/6/964

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.