基于改进GA-BP的移动通信用户流失预测算法

BP神经网络(BPNN)模型对移动通信用户流失的预测有较好的效果，但其全局搜索能力相对较弱，对初始网络权重非常敏感，因此本文通过对用户通信行为的分析，提出一种基于改进GA-BP的移动用户流失预测算法:用改进的遗传算法对BPNN的权值和阈值进行初始化，从而提高预测模型的准确率.改进的遗传算法采用一种自适应的交叉概率和变异概率计算策略，提高了遗传算法寻找全局最优解的能力.通过对比实验发现，本文构建的移动用户流失预测模型，在预测准确率上有着很好的表现.

Mobile Communications Customer Churn Prediction Algorithm Based on Improved GA-BP Network

A customer churn prediction model based on BP neural network(BPNN)has achieved well enough results. However， it has relatively weak global search ability and is very sensitive to the initial network weights. A prediction algorithm based on improved genetic algorithm(IGA)and BPNN(IGA-BP)is proposed by analyzing users’communication behavior， where the weights and thresholds of BPNN are initialized with IGA， thus improving the accuracy of the prediction model. The improved algorithm adopts a self-adapting probability of crossover and mutation， which enhances the global optimum search ability of GA. The proposed IGA-BP model has obvious improvement on customer churn prediction， compared with existing algorithms.