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• W2018979850 abstract "Customer churn analysis and prediction play an important role in customer relationship management and improve benefit of enterprise. According to the bank's customer churn data which is large scale and imbalance, this paper presented a support vector machine model to predict customer churn. The method was compared with artificial neural network, decision tree, logistic regression and naive Bayesian classifier regarding customer churn prediction for a commercial bank's VIP customers. It is found that the method has the best accuracy rate, hit rate, covering rate and lift coefficient, and provides an effective measurement for bank's customer churn prediction." @default.
• W2018979850 created "2016-06-24" @default.
• W2018979850 creator A5018427469 @default.
• W2018979850 creator A5082392473 @default.
• W2018979850 date "2008-10-01" @default.
• W2018979850 modified "2023-09-26" @default.
• W2018979850 title "Bank Customer Churn Prediction Based on Support Vector Machine: Taking a Commercial Bank's VIP Customer Churn as the Example" @default.
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• W2018979850 doi "https://doi.org/10.1109/wicom.2008.2509" @default.
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