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.
出处
《广东工业大学学报》
CAS
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)
关键词
贝叶斯分类
电信数据
增量学习
客户流失
预测
Bayesian classification
telecommunication data
incremental learning method
customer churn
prediction
