Evaluation of Logistics Reputation Based on Principal Component Analysis and BP Neural Network
Ruijun Zhang, Kui Fu
Available Online October 2017.
- https://doi.org/10.2991/icesem-17.2017.38How to use a DOI?
- BP neural network; E-commerce sellers; Principal component analysis; credit rating
- In order to reduce the risk of the buyers' purchase and avoid the problems of future returns and replacement after purchase, we need to sort out the credit of the seller of the electricity supplier to implement different purchase risk control strategies. Based on the principal component analysis and BP neural network, this paper selects the evaluation results of e-commerce buyers, combines with questionnaire surveys and expert analysis to establish a more referential index system so that the credit evaluation model of e-commerce sellers is established. The case study shows that the index system and the mathematical model can accurately judge the seller's credit rating, and have a strong guiding significance for buyers and enterprises to choose partners.
- Open Access
- This is an open access article distributed under the CC BY-NC license.
Cite this article
TY - CONF AU - Ruijun Zhang AU - Kui Fu PY - 2017/10 DA - 2017/10 TI - Evaluation of Logistics Reputation Based on Principal Component Analysis and BP Neural Network BT - Proceedings of the 2017 International Conference on Education Science and Economic Management (ICESEM 2017) PB - Atlantis Press SP - 164 EP - 169 SN - 2352-5398 UR - https://doi.org/10.2991/icesem-17.2017.38 DO - https://doi.org/10.2991/icesem-17.2017.38 ID - Zhang2017/10 ER -