Research Article
VDIF-M: Multi-label Classification of Vehicle Defect Information Collection Based on Seq2seq Model
@INPROCEEDINGS{10.1007/978-3-030-28468-8_8, author={Xindong You and Yuwen Zhang and Baoan Li and Xueqiang Lv and Junmei Han}, title={VDIF-M: Multi-label Classification of Vehicle Defect Information Collection Based on Seq2seq Model}, proceedings={Mobile Computing, Applications, and Services. 10th EAI International Conference, MobiCASE 2019, Hangzhou, China, June 14--15, 2019, Proceedings}, proceedings_a={MOBICASE}, year={2019}, month={9}, keywords={Multi-label classification Seq2seq Label generation Deep learning}, doi={10.1007/978-3-030-28468-8_8} }- Xindong You
Yuwen Zhang
Baoan Li
Xueqiang Lv
Junmei Han
Year: 2019
VDIF-M: Multi-label Classification of Vehicle Defect Information Collection Based on Seq2seq Model
MOBICASE
Springer
DOI: 10.1007/978-3-030-28468-8_8
Abstract
Classification and treatment of vehicle defect complaint data is an important link in the process of vehicle recall. Traditionally, the complaint data is classified by keyword matching method based on defect label library during the process of dealing with vehicle complaint data, which heavily relies heavily on the quality of the vehicle defect label library. The speed of traditional classification methods is rapid, but the accuracy is low. We transform the classification task of vehicle complaint data into a multi-label classification problem. Multi-label classification of vehicle defect information collection based on seq2seq model named VDIF-M is proposed in this paper. Firstly, a synonymous vehicle defect description label library is constructed based on the vehicle defect description data and vehicle domain corpus collected from various channels. Then a seq2seq model is proposed to solve the problem of multi-label classification of vehicle complaint data, which fuses the distribution relationship between labels. Substantial experimental results show that the proposed method outperforms previous methods in multi-label classification of vehicle complaint data.