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VDRF: Sensing the Defect Information to Risk Level of Vehicle Recall based on Bert Communication Model

Xindong You¹, Jiangwei Ma¹, Yuwen Zhang¹, Xueqiang Lv¹ and Junmei Han²

1. Beijing Key Laboratory of Internet Culture and Digital Dissemination Research
   Beijing Information Science & Technology University, Beijing, 100101, China
2. Laboratory of Complex systems, Institute of Systems Engineering. AMS. PLA
   Beijing, 100029 China

Abstract

The recall of defective automobile products is one of the important measures to promote the quality of product quality and protect consumers' pyhsical safety and property security. In order to assess the risk level of defect cases, automobile recall management experts need to analyze and discuss the defect information by personal. A risk level prediction method based on language pre-training Bert model is proposed in this paper, which can transform the defect information into rick level of the vehicle and then predict vehicle recall automatically, in which a seq2seq model is proposed to multi-label the vehicle complaint data. The outputs of the seq2seq model combined with other static and dynamic information are used as the input of the Bert communication model. Substantial comparative experiments of different feature combinations on different methods show that the proposed VDRF method achieves F1 value with 79% in vehicle recall risk prediction, which outperforms the traditional method.

Key words

Bert communication model, defect information transforming, multilabel classification, risk level prediction

Digital Object Identifier (DOI)

https://doi.org/10.2298/CSIS190903021Y

Publication information

Volume 17, Issue 3 (October 2020)
Year of Publication: 2020
ISSN: 2406-1018 (Online)
Publisher: ComSIS Consortium

Full text

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How to cite

You, X., Ma, J., Zhang, Y., Lv, X., Han, J.: VDRF: Sensing the Defect Information to Risk Level of Vehicle Recall based on Bert Communication Model. Computer Science and Information Systems, Vol. 17, No. 3, 795–817. (2020), https://doi.org/10.2298/CSIS190903021Y