基于缺失数据BN参数学习的电信流失客户预测算法

赵宇翔; 卢光跃; 王航龙; 李四维

doi:10.11959/j.issn.1000-0801.2018018

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基于缺失数据BN参数学习的电信流失客户预测算法

研究与开发 | 更新时间：2024-06-05

- 基于缺失数据BN参数学习的电信流失客户预测算法
- A prediction algorithm of telecom customer churn based on Bayesian network parameters learning under incomplete data
- 电信科学 2018年34卷第1期页码：52-60
- 作者机构：
- 作者简介：
  
  [ "赵宇翔（1993−），男，西安邮电大学陕西省信息通信网络及安全重点实验室硕士生，主要研究方向为数据挖掘。" ]
  [ "卢光跃（1971−），男，博士，西安邮电大学陕西省信息通信网络及安全重点实验室教授，主要研究方向为信号与信息处理、认知无线电和大数据分析。" ]
  [ "王航龙（1989−），男，西安邮电大学陕西省信息通信网络及安全重点实验室硕士生，主要研究方向为数据挖掘。" ]
  [ "李四维（1989−），男，西安邮电大学陕西省信息通信网络及安全重点实验室硕士生，主要研究方向为数据挖掘。" ]
- 基金信息：
  
  陕西省工业科技攻关项目;Industrial Research Project of Science and Technology Department of Shaanxi Province(2015GY-013);陕西省工业科技攻关项目(2016GY-113)
- DOI：10.11959/j.issn.1000-0801.2018018
  中图分类号： TP181
- 网络出版日期：2018-01，
  
  纸质出版日期：2018-01-20
- 稿件说明：
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赵宇翔, 卢光跃, 王航龙, 等. 基于缺失数据BN参数学习的电信流失客户预测算法[J]. 电信科学, 2018,34(1):52-60.

Yuxiang ZHAO, Guangyue LU, Hanglong WANG, et al. A prediction algorithm of telecom customer churn based on Bayesian network parameters learning under incomplete data[J]. Telecommunications science, 2018, 34(1): 52-60.
赵宇翔, 卢光跃, 王航龙, 等. 基于缺失数据BN参数学习的电信流失客户预测算法[J]. 电信科学, 2018,34(1):52-60. DOI： 10.11959/j.issn.1000-0801.2018018.

Yuxiang ZHAO, Guangyue LU, Hanglong WANG, et al. A prediction algorithm of telecom customer churn based on Bayesian network parameters learning under incomplete data[J]. Telecommunications science, 2018, 34(1): 52-60. DOI： 10.11959/j.issn.1000-0801.2018018.

摘要

针对电信客户流失预测问题，在数据缺失情况下，基于贝叶斯网络（Bayesian network，BN），用最近邻算法填补缺失数据，并将两类定性约束融入贝叶斯网络参数学习过程，用以提高流失客户预测精度。仿真及实际数据分析结果表明，所提算法较经典的期望最大化（expectation maximization，EM）算法有明显优势，在牺牲代价较小的忠诚客户预测精度的情况下，得到了更高的流失客户预测精度。

Abstract

Aiming at prediction of telecom customer churn

a novel method was proposed to increase the prediction accuracy with the missing data based on the Bayesian network.This method used k-nearest neighbor algorithm to fill the missing data and adds two types of monotonic influence constraints into the process of learning Bayesian network parameter.Simulations and actual data analysis demonstrate that the proposed algorithm obtains higher prediction accuracy of churn customers with the loss of less cost prediction accuracy of loyal customers

outperforms the classic expectation maximization algorithm.

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references

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