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《Chinese Journal of Management》 2011-02
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Customer Churn Research Based on Multiple Classifier fusing Rough Sets-Neural Network-Artificial Bee Colony Algorithm

LUO Bin SHAO Peiji LUO Jinyao LIU Duyu XIA Guoen(University of Electronic Science and Technology,Chendu,China)  
In this paper,on account of the complexity of customer churn in communication industry,fusing the advantages of rough sets,neural network and artificial bee colony algorithm(ABC),a new customer churn prediction model is put forward,which is a linear-fused multiple classifier based on rough sets theory,neural network and artificial bee colony algorithm.Firstly,it completes the unsupervised separation of the continuous attributes using SOM;secondly,it reduces the discrete attributes using rough sets theory;thirdly,it builds four sub-classifiers on the reduced attribute set using BP neural network,radial basis function neural network(RBF),ELMAN neural network and generalized regression neural network(GRNN);finally,it integrates linearly the prediction results from the sub-classifiers and optimize the weights by ABC.Through applying the model to customer churn research in a telecommunication enterprise,the experiments results suggest that the integration technique is feasible and very efficient.
【Fund】: 国家自然科学基金资助项目(70801021);; 中国博士后科学基金资助项目(20080431276);; 教育部人文社会科学基金资助项目(08JC630019)
【CateGory Index】: F274;F626
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