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Predictions model of customer churn in E-commerce based ononline sequential optimization extreme learning machine(PDF)


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Predictions model of customer churn in E-commerce based ononline sequential optimization extreme learning machine
Yang Li12
1.School of Management,Hefei University of Technology,Hefei 230009,China; 2.Economic and Management School,Anhui Vocational College of Defense Technology,Lu’an 237011,China
E-commerce customer churn cloud computing technology prediction model extreme Learning Machine
In order to improve the customer churn prediction accuracy of E-commerce customer,and single machine model cannot effectively predict customer churn of massive E-commerce customers,this paper proposes a novel prediction model of customer churn in E-commerce based on online sequential optimization extreme learning machine. Firstly,the Map/Reduce model of cloud computing is used to segment the amount of customer churn in E-commerce,and multiple training subsets are obtained; secondly extreme learning machine is used to model each training subset of E-commerce customer churn,and the prediction results of training subsets are combined to get the final forecast results of customer churn in E-commerce; at last the validity of E-commerce customer churn prediction model is tested by example. The results show that the proposed model improves the prediction accuracy of customer churn in E-commerce,and the training time of E-commerce customer churn modeling has greatly reduced,improving the churn prediction speed of E-commerce customers.


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Last Update: 2019-02-28