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A NeuroFuzzy Classifier for Customer Churn Prediction

Author(s): Hossein Abbasimehr | Mostafa Setak | M. J. Tarokh

Journal: International Journal of Computer Applications
ISSN 0975-8887

Volume: 19;
Issue: 08;
Start page: 35;
Date: 2011;
Original page

Keywords: Churn Prediction | Data mining | ANFIS | Fuzzy C-means | Subtractive clustering

Churn prediction is a useful tool to predict customer at churn risk. By accurate prediction of churners and nonchurners, a company can use the limited marketing resource efficiently to target the churner customers in a retention marketing campaign. Accuracy is not the only important aspect in evaluating a churn prediction models. Churn prediction models should be both accurate and comprehensible. Therefore, Adaptive Neuro Fuzzy Inference System 'ANFIS' as neurofuzzy classifier is applied to churn prediction modeling and benchmarked to traditional rulebased classifier such as C4.5 and RIPPER. In this paper, we have built two ANFIS models including ANFISSubtractive 'subtractive clustering based fuzzy inference system 'FIS'' and ANFISFCM 'fuzzy Cmeans 'FCM' based FIS' models. The results showed that both ANFISSubtractive and ANFISFCM models have acceptable performance in terms of accuracy, specificity, and sensitivity. In addition, ANFISSubtractive and ANFISFCM clearly induce much less rules than C4.5 and RIPPER. Hence ANFISSubtractive and ANFISFCM are the most comprehensible techniques tested in the experiments. These results indicate that ANFIS shows acceptable performance in terms of accuracy and comprehensibility, and it is an appropriate choice for churn prediction applications.
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