Hierarchical Neural Regression Models for Customer Churn Prediction

As customers are the main assets of each industry, customer churn prediction is becoming a major task for companies to remain in competition with competitors. In the literature, the better applicability and efficiency of hierarchical data mining techniques has been reported. This paper considers three hierarchical models by combining four different data mining techniques for churn prediction, which are backpropagation artificial neural networks (ANN), self-organizing maps (SOM), alpha-cut fuzzy c-means (α-FCM), and Cox proportional hazards regression model. The hierarchical models are ANN

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