An efficient system for customer churn prediction through particle swarm optimization based feature selection model with simulated annealing

Churn prediction in telecom has gained a huge prominence in the recent times due to the extensive interests exhibited by the stakeholders, large number of competitors and huge revenue losses incurred due to churn. Predicting telecom churn is challenging due to the voluminous and sparse nature of the data. This paper presents a technique for the telecom churn prediction that employs particle swarm optimization (PSO) and proposes three variants of PSO for churn prediction namely, PSO incorporated with feature selection as its pre-processing mechanism, PSO embedded with simulated annealing and finally PSO with a combination of both feature selection and simulated annealing. The proposed classifiers were compared with decision tree, naive bayes, K-nearest neighbor, support vector machine, random forest and three hybrid models to analyze their predictability levels and performance aspects. Accuracy, true positive rate, true negative rate, false positive rate, Precision, F-Measures, receiver operating characteristic and precision-recall plots were used as performance metrics. Experiments reveal that the performance of metaheuristics was more efficient and they also exhibited better predictability levels.