Evaluation of Logistics Reputation Based on Principal Component Analysis and BP Neural Network

DOI

10.2991/icesem-17.2017.38How to use a DOI?

Keywords

BP neural network; E-commerce sellers; Principal component analysis; credit rating

Abstract

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.

Copyright

© 2017, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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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  - 10.2991/icesem-17.2017.38
ID  - Zhang2017/10
ER  -