A prediction algorithm of telecom customer churn based on Bayesian network parameters learning under incomplete data - Yuxiang ZHAO; Guangyue LU; Hanglong WANG; Siwei LI (Dianxin kexue)
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.