Terminal Replacement Prediction Based on Deep Belief Networks

To help telecommunications operators accurately predict the terminal replacement behavior, and improve the success rate of marketing and the accuracy of resources devoting, huge user consumption data are used to build Deep Belief Network. The deep features that characterize the terminal replacement behavior are learned, through which a terminal replacement prediction model is conducted. Experiments are carried out on real data set, and the prediction accuracy is over 82%. It is better than three others models based on 1 Nearest Neighbors, Support Vector Machines and Neural Network. The experiments results show that the features obtained by deep learning are more descriptive for predicting terminal replacement behavior.