An SVM based churn detector in prepaid mobile telephony
An SVM based churn detector in prepaid mobile telephony
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DOI：
10.1109/ictta.2004.1307830
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发表时间：
2004-04
期刊：
影响因子：
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通讯作者：
Cédric Archaux;Arnaud Martin;A. Khenchaf
中科院分区：
文献类型：
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作者：
Cédric Archaux;Arnaud Martin;A. Khenchaf
预付费移动的电话的上下文是特定的，因为客户与其运营商没有合同联系，因此可以在不通知的情况下停止其活动。为了估计可以对每个客户进行的保留工作，运营商必须将具有强烈流失风险的客户与其他客户区分开来。这项工作提出了一个数据挖掘应用程序，导致流失检测器。我们将历史上应用于该问题的人工神经网络（ANN）与在分类方面特别有效并适应有噪数据的支持向量机（SVM）进行了比较。因此，本文的目的是比较SVM和ANN在预付费蜂窝电话流失检测中的应用。我们表明，SVM比ANN在这个特定的问题上给出了更好的结果。
The context of prepaid mobile telephony is specific in the way that customers are not contractually linked to their operator and thus can cease their activity without notice. In order to estimate the retention efforts which can be engaged towards each individual customer, the operator must distinguish the customers presenting a strong churn risk from the other. This work presents a data mining application leading to a churn detector. We compare artificial neural networks (ANN) which have been historically applied to this problem, to support vectors machines (SVM) which are particularly effective in classification and adapted to noisy data. Thus, the objective of this article is to compare the application of SVM and ANN to churn detection in prepaid cellular telephony. We show that SVM gives better results than ANN on this specific problem.