Computing efficient features using rough set theory combined with ensemble classification techniques to improve the customer churn prediction in telecommunication sector

Rough set theory (RST) can be viewed as one of the classical set theory for handling with imprecision knowledge. The theory has discovered applications in numerous areas, for example, engineering, industries, environment and others. Churn in telecommunication sector, customer switching from one service provider to another. Predicting telecom customer churn is challenging due to the huge and inconsistent nature of the data. Churn prediction is crucial for telecommunication companies in order to build an efficient customer retention plan and apply successful marketing strategies. In this article, a methodology is proposed using RST to identify the efficient features for telecommunication customer churn prediction. Then the selected features are given to the ensemble-classification techniques such as Bagging, Boosting, Random Subspace. In this work the duke university-churn prediction data set is considered for performance evaluation and three sets of experiments are performed. Finally the performance of the proposed model is evaluated based on the following metrics such as true churn, false churn, specificity, precision and accuracy and it is identified that Proposed system designed with combining attribute selection with ensemble classification techniques works fine with classification accuracy of 95.13% compared to any single model.