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

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。

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.