An algorithm for predicting customer churn via BP neural network based on rough set

被引：0

作者：

Xu, E. ^{[1
]}

Shao Liangshan ^{[2
]}

Gao Xuedong ^{[3
]}

Zhai Baofeng ^{[4
]}

机构：

[1] Liaoning Inst Technol, Dept Comp Sci, Jinzhou 121001, Peoples R China

[2] Liaoning Tech Univ, Sch Management, Fuxing 123000, Peoples R China

[3] Beijing Univ Sci & Technol, Sch Management, Beijing 100083, Peoples R China

[4] Liaoning Inst Technol, Software Sch, Jinzhou 121001, Peoples R China

来源：

APSCC: 2006 IEEE ASIA-PACIFIC CONFERENCE ON SERVICES COMPUTING, PROCEEDINGS | 2006年

关键词：

D O I：

暂无

中图分类号：

TP39 [计算机的应用];

学科分类号：

081203 ; 0835 ;

摘要：

To solve the prediction of customer churn, the paper proposed a new algorithm. Based on rough set theory, the algorithm used the consistency of condition attributes and decision attributes in information table, and the conception of super cube and scan vector to discretize the continuous attributes, reduce the redundant attributes. And furthermore, it took BP neural network as the calculating tool to predict customer churn. The experimental results showed the refined data by rough set was more concise and more convenient to he applied in BP neural network, whose prediction result was more accurate. So, the algorithm via BP neural network based on rough set theory is efficient and effective.

引用

页码：47 / +

页数：2

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