改进的贝叶斯分类方法在电信客户流失中的研究与应用  

Research and Application of Improved Bayes Algorithm for the Telecommunication Customer Churn

作　　者：杨婷[1] 滕少华[1] 

机构地区：[1]广东工业大学计算机学院,广东广州510006

出　　处：《广东工业大学学报》2015年第3期67-72,共6页Journal of Guangdong University of Technology

基　　金：教育部重点实验室基金资助项目(110411);广东省自然科学基金资助项目(10451009001004804;9151009001000007);广东省科技计划项目(2012B091000173;2013B090200017;2013B010401029;2013B010401034);广州市科技计划项目(2012J5100054;2013J4500028)

摘　　要：随着电信市场竞争日益加剧,客户流失成为运营商关注的焦点问题之一.针对电信数据量庞大且有时间序列的特点,提出一种改进的贝叶斯分类方法来研究电信客户流失问题,通过对不同属性加权改进了朴素贝叶斯分类器默认每个属性对分类结果影响相同的假设,进一步探讨了应用增量学习方法来应对不断增加的数据,以改善分类器的准确率.实验结果表明,本文的方法有较高的准确率.With the increasing competition of telecom market, customer churn became one of the focused problems.Because the telecommunication data is huge and has the characteristic of time series, this pa-per proposes an improved Bayesian classification to study the customer churn problem.The improved Bayesian classification model is designed to make up for the shortcomings of the former Bayes which as-sumed that each attribute has the same effect on the classification results.Furthermore, by coping with the increasing data, this paper explores the incremental learning method to improve the accuracy of the classifier.The experimental results show that the proposed method has higher accuracy.

关 键 词：贝叶斯分类 电信数据 增量学习 客户流失 预测 

分 类 号：TP393[自动化与计算机技术—计算机应用技术]