Abstract
the customer churn is a crucial activity in the competitive and rapidly growing telecommunication industry. Due to the high cost of acquiring a new customer, customer churn prediction is one of the greatest importance for project managers. It is important to forecast customer churn behavior in order to retain those customers that will churn or possibly may churn. This study is another attempt which makes use of rough set theory as one-class classifier and multi-class classifier to reveal 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 out of four 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.