Model of customer churn prediction on support vector machine

被引：0

作者：

Guangxi University of Finance and Economics, Nanning 530003, China ^{[1
]}

不详 ^{[2
]}

机构：

来源：

Xitong Gongcheng Lilum yu Shijian | 2008年 / 1卷 / 71-77期

基金：

中国国家自然科学基金;

关键词：

Forecasting - Learning algorithms - Risk analysis - Support vector machines;

D O I：

10.1016/s1874-8651(09)60003-x

中图分类号：

学科分类号：

摘要：

To improve the prediction abilities of machine learning methods, a support vector machine(SVM) on structural risk minimization was applied to customer churn prediction. The method was compared with artificial neural network, decision tree, logistic regression and naive Bayesian classifier regarding customer churn prediction for home and foreign telecommunication carriers. It is found that the method has the best accuracy rate, hit rate, covering rate and lift coefficient, and provides an effective measurement for customer churn prediction.

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