摘要
将K近邻分类法和支持向量机分类法结合起来,给出一种电信客户流失预测方法,即对边界样本采用加权K近邻分类,而对非边界样本采用改进的支持向量机分类。在公开不平衡数据集和电信数据集上的实验可验证所给方法有效,且能提高少数类的检测精度和总体评价指标。
Combining K-nearest neighbor classification and support vector machine classification,a telecom customer churn prediction method is presented.According to this method,the boundary samples are classified by the weighted K-nearest neighbor,and the non boundary samples are classified by the improved support vector machine respectively.Experiments on open unbalanced data sets and telecommunication data sets show that,the proposed method is effective and can improve the overall evaluation index,especially improve the detection accuracy of the minority.
作者
卢光跃
王航龙
李创创
赵宇翔
李四维
LU Guangyue, WANG Hanglong, LI Chuangchuang,ZHAO Yuxiang, LI Siwei(Shaanxi Key Laboratory of Information Communication Network and Security,Xi＇an University of Posts and Telecommunications,Xi＇an 710121,China)
出处
《西安邮电大学学报》
2018年第2期1-6,共6页
Journal of Xi’an University of Posts and Telecommunications
基金
陕西省工业科技攻关项目(2015GY-013
2016GY-113)
关键词
客户流失
支持向量机
K近邻
不均衡数据集
customer churn
support vector machine
K nearest neighbor
unbalanced data set
