Proceedings of the 2018 International Conference on Transportation & Logistics, Information & Communication, Smart City (TLICSC 2018)

Research on Customer Churn Prediction Method based on Variable Precision Rough set and BP Neural Network

Authors
Jing Gong, Jing Ju, Zhe Sun, Chun Ying, Shuhua Tan, Zhixin Sun
Corresponding Author
Jing Gong
Available Online December 2018.
DOI
https://doi.org/10.2991/tlicsc-18.2018.46How to use a DOI?
Keywords
variable precision; rough set; information entropy; BP neural network; Adam algorithm; logistics large customer; customer churn prediction.
Abstract
BP neural network and rough set theory play an important role in the field of prediction. In view of the present situation of customer churn in logistics industry, this paper combines rough set and BP neural network to forecast customer attrition behavior in logistics industry. Firstly, using rough sets to extract rules from normal and abnormal customers to distinguish customer classes in logistics industry. Discrete processing of information entropy of extracted logistics customer attributes based on rough sets being good at handling discrete data. Finally, according to the strong mobility of logistics customers, Adam algorithm is introduced to build an adaptive BP neural network training model. The model proposed in this paper is more suitable for real-time data processing. The experiment proves that the method is feasible and efficient.
Open Access
This is an open access article distributed under the CC BY-NC license.

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Proceedings
Part of series
Advances in Intelligent Systems Research
Publication Date
December 2018
ISBN
978-94-6252-621-1
ISSN
1951-6851
DOI
https://doi.org/10.2991/tlicsc-18.2018.46How to use a DOI?
Open Access
This is an open access article distributed under the CC BY-NC license.

Cite this article

TY  - CONF
AU  - Jing Gong
AU  - Jing Ju
AU  - Zhe Sun
AU  - Chun Ying
AU  - Shuhua Tan
AU  - Zhixin Sun
PY  - 2018/12
DA  - 2018/12
TI  - Research on Customer Churn Prediction Method based on Variable Precision Rough set and BP Neural Network
PB  - Atlantis Press
SP  - 287
EP  - 293
SN  - 1951-6851
UR  - https://doi.org/10.2991/tlicsc-18.2018.46
DO  - https://doi.org/10.2991/tlicsc-18.2018.46
ID  - Gong2018/12
ER  -