Improved Ensemble Learning Model by Swarm Intelligence for Mobile Subscribers’ Churn Prediction

In today’s competitive world, companies need to analyze, identify and predict the behaviorof their customers and respond to their demands earlier than their competitors. Moreover, in manyindustries such as mobile telecommunications, the cost of maintaining existingcustomers (customer retention)is much lower than the cost of attracting a new customer. Therefore,the problem of identifying customers who are going to leave the company, so-called Customer Churn Prediction (CCP),and preventing themby offering Incentivesis essential in these industries. In this direction, researchers have presentedefficient methods using data mining and artificial intelligence tools to identify potentialchurners. In order to improve the process of predicting customer churn, in this paper we propose a novelensemble learningbased approach, in which the Gray Wolf Optimization(GWO) algorithm is utilized to select the effective features and also adjust the hyper-parameters in the proposed model. We have implemented our proposed model using Python and simulated it on the IBM Telco dataset to evaluate itsperformanceand compared the obtained results with existingmethods using common evaluation criteria including accuracy, precision, recall,and AUC. The experimentalresults show the superiority of the proposed method over other evaluated solutions.