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Customer Churn Prediction Model Fusing Multiple Classifier Based on Cost Sensitivity Study Using Ant Colony Optimization
LUO Bin, SHAO Pei-ji, LUO Jin-yao, LIU Du-yu, XIA Guo-en
2010, 18 (3):
58-67.
According to the complexity of customer churn prediction in Telecom,integrating the characters,such as the excellent indicative of the self-organizing neural network(SOM)when discretizing continuous at tributes,the outstanding capability of rough set theory(RS)when reducing the attributes,and the feature of the ant colony optimization(ACO)when searching atrandom globally,based on the technique of model integration and the theory of cost sensitivity study,a new customer churn model is proposed,i.e.fusing multiple classifiers based on cost sensitivity study using ant colony optimization(ACO). When constructing the model,there are four steps:(1)Discretizing the continuous at tributes unsupervis edly using SOM;(2)Reducing the discrete attributes according to the importance of the attributes using RS;(3)Building four sub classifiers on the reduced attributes sets using four completely different classification techniques including NaiveBayes,Logistic Regression,Multilayer Perceptron and Decision Tree,respectively;(4)Fusing the sub classifiers,that based on cost sensitive theory,and integrated four models linearly,which weight sobtained through the ant colony optimization(ACO).Applying the model to customer churn research in a telecom munication enterprise,the experiment results suggest that the fusing technique is feasible and very efficient.
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