LAUSR

Nowadays, telecom industry faces fierce competition in satisfying its customers. This competition thus requires an efficient churn prediction system to identify customers who are ready to quit. Such churn customers are then retained through addressing relevant reasons identified by the churn prediction system. Therefore, now the role of churn prediction system is not only restricted to accurately predict churners but also to interpret customer churn behavior. In this paper, searching capabilities of genetic programming (GP) and classification capabilities of AdaBoost are integrated in order to evolve a high-performance churn prediction system having better churn identification abilities. For this, frequently selected features in various GP expressions evaluated through AdaBoost based learning, are marked and analyzed. Moreover, the issue of imbalance present in telecom datasets is also addressed through particle swarm optimization (PSO) based undersampling method, which provides unbiased distribution of training set to GP-AdaBoost based prediction system. Particle swarm optimization based undersampling method in combination with GP-AdaBoost results a churn prediction system (ChP-GPAB), which offers better learning of churners and also identifies underlying factors responsible for churn behavior of customers. Two standard telecom data sets are used for evaluation and comparison of the proposed ChP-GPAB system. The results show that the proposed ChP-GPAB system yields 0.91 AUC and 0.86 AUC on Cell2Cell and Orange datasets, in addition to identifying the reasons of churning.