A Novel Architecture and Machine Learning Algorithm for the Prediction of User Equipment Replacing

Abstract

The prediction of User Equipment replacing is worth of research both for telecom operators and mobile phone companies. This paper designs a machine learning prediction of User Equipment replacing (MLPUser EquipmentC) architecture and a data mining algorithm called CQSFL-LR (Composite-parameter Quantum-inspired Shuffled Frog Leaping Logistic Regression), aiming at researching the factors and their weight respectively of a telecom user whether will replace his cellphone or not. Experiment shows the proposed CQSFL-LR algorithm has better performance in accuracy and precision compared with traditional Logistic Regression, proving the superiority of CQSFL-LR. The experiment also shows MLPUser EquipmentC architecture can predict User Equipment replacing, providing marketing guidance to telecom operators and mobile phone companies.