Mobile communication user loss prediction method based on particle classification and BP neural network

Abstract

The invention relates to a mobile communication user loss prediction method based on particle classification and a BP neural network. The method comprises: communication record data of a mobile user are collected; data pretreatment is carried out to obtain a needed sample data set; a BP neural network structure is established; on the basis of an improved particle swarm optimization (PSO) algorithm, a weight matrix and a threshold matrix of the BP neural network are initialized; a BP neural network corresponding to a particle having best fitness is trained to obtain a mobile communication user loss model; and mobile communication user loss prediction is carried out by using the mobile communication user loss model. According to the invention, the weight matrix and the threshold matrix of the BP neural network are initialized by combining application of a particle classification optimization (PCO) algorithm and a PFC process, so that the weight matrix and the threshold matrix of the BP neural network are close to global optimal values and thus the mobile user loss prediction accuracy of the BP neural network is improved.

Description

technical field [0001] The invention relates to the fields of big data and artificial intelligence, in particular to a mobile communication user loss prediction method based on particle classification and BP neural network. Background technique [0002] Nowadays, people's life is almost inseparable from mobile phones. As mobile phones provide more and more services, competition between mobile operators has become more intense. It is hard to imagine processing the data generated by hundreds of millions of mobile phone users every month, let alone extracting useful information from such a huge data set to predict user churn. Prediction of the loss of mobile communication users is very critical to the formulation of appropriate marketing strategies for mobile communication networks. Only by accurately predicting the users who will be lost can an appropriate marketing strategy be formulated to restore the users who will be lost, achieve the best benefits, and improve mobile com...

AI Technical Summary

Problems solved by technology

However, when these methods are applied to big data classification and prediction, they have high time and space complexity. Usually, when building a prediction model, only by analyzing a small amount of data, the built model is difficult to describe the characteristics of all users. It is inevitable that there will be misjudgments on the issue of user churn prediction

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