摘要
为了解决电信行业客户流失预测模型中流失者和未流失者比例偏斜问题,模型依据数据挖掘原理,以CRISP-DM(Cross-industry Standard Process for Data Mining)建模过程为框架,采用了多基决策树联合决策的思想。模型避免了训练出一棵"空"决策树,把所有客户都预测为未流失的问题。与单个分类器相比,提高了预测模型的查准率和泛化能力。
In order to well resolve the highly skewed class distribution between churns and no-churns, the customers churn prediction model is realized according to the CRISP-DM （ Cross-industry Standard Process for Data Mining） framework. The multi-classifier class-combiner approach is adopted. The model could not result in a ＇ null＇ prediction system that simply predicts all instances as non-churners. Compared with a single classifier, the accuracy and generalization of the model are improved.
出处
《计算机与现代化》
2010年第5期5-7,11,共4页
Computer and Modernization
基金
山西大同大学2008年度青年科研基金资助项目(2008Q15)
关键词
客户流失预测
决策树
多基决策树联合决策
数据挖掘
customers churn prediction
decision tree
multi-classifier class-combiner
data mining
