【作者】 谢伟；

【导师】 李兴国；

【作者基本信息】 合肥工业大学 ， 信息管理与信息系统， 2009， 硕士

【摘要】 我国移动通信市场在经历了客户数量高速膨胀的阶段之后,目前新客户增加放缓,受到各种促销方案的影响,加上简单的入网手续,顾客的随机性大幅度上升,各移动运营商均面临客户大量流失(churn)问题。因此,预测出有流失倾向的客户已成为移动通信企业的迫切需要。本文主要研究支持向量机(Support Vector Machine SVM)算法在移动通信业客户流失预测中的应用。本文针对移动客户的多类别特点以及不同类别客户的数据集分布不平衡,给出了DAG-SVM+CW-SVM算法,建立了移动客户流失预测模型。分别用一对多( 1-V-R SVM )、一对一( 1-V-1 SVM )、有向树( DAG-SVM )与DAG-SVM+CW-SVM算法对移动客户进行流失预测,并用接受者操作特性曲线(Receiver Operating Characteristic Curve简称ROC曲线)、ROC曲线下的面积(the Area under the Receiver Operating Characteristic Curve简称AUC值)、提升度(lift)对四种算法进行评价对比。实验表明DAG-SVM+CW-SVM算法不仅能够处理数据多类别分类问题而且能够有效的解决数据集不平衡对预测结果造成的影响,具有较好的预测效果。更多还原

【Abstract】 The mobile communication market of our country had experienced rapid expansion, now new customers increase slowly, by the impact of various marketing programs, coupled with simple access procedures, the randomness of customers is substantial increase, and the mobile operators are facing on the huge problem of customers churn. Therefore, predict churn has become an urgently needed of mobile communication enterprises. On this need the investigation of predictive system of China Mobile churn based on Data Mining introduced in this paper.The main research of this paper is application of Support Vector Machine (SVM) algorithm in the mobile communications enterprises churn prediction. According to the churn data which is imbalance and have three classes, the class weighted SVM model CW-SVM is applied, an improved SVM- DAG-SVM+CW-SVM was presented to predict churner, which was better arithmetic performance than 1-V-R SVM , 1-V-1 SVM , DAG-SVM. It was demonstrated that this algorithm was suitable for solving multi-class data and imbalanced data with higher precision.更多还原