Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
An Algorithm for Predicting Customer Churn via BP Neural Network Based on Rough Set
APSCC '06 Proceedings of the 2006 IEEE Asia-Pacific Conference on Services Computing
TreeLogit Model for Customer Churn Prediction
APSCC '06 Proceedings of the 2006 IEEE Asia-Pacific Conference on Services Computing
A novel decision rules approach for customer relationship management of the airline market
Expert Systems with Applications: An International Journal
A novel classification method based on hypersurface
Mathematical and Computer Modelling: An International Journal
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Decision tree, neural network and logistic regression were applied frequently as models of customer churn prediction, but the application of them has been mature and they are difficult to be improved. In this paper, Bayesian Networks, Support Vector Machines, Rough Sets and Survival Analysis were selected for experimental comparison study. An integrated contrast among the four models from the applicability of model in theory and experimental comparison has been processed. Overall, of the four models the Bayesian network model performed best while the Survival analysis did worst.