Customer Churn Prediction in Telecommunication Industry: With and without Counter-Example

Abstract

This study contributes to formalize customer churn prediction where rough set theory is used as one-class classifier and multi-class classifier to investigate the trade-off in the selection of an effective classification model for customer churn prediction. Experiments were performed to explore the performance of four different rule generation algorithms (i.e. exhaustive, genetic, covering and LEM2). It is observed that rough set as one-class classifier and multi-class classifier based on genetic algorithm yields more suitable performance as compared to the other three rule generation algorithms. Furthermore, by applying the proposed techniques (i.e. Rough sets as one-class and multi-class classifiers) on publicly available dataset, the results show that rough set as a multi - class classifier provides more accurate results for binary/multi-class classification problems.