计算机与现代化 ›› 2010, Vol. 1 ›› Issue (5): 5-7,11.doi: 10.3969/j.issn.1006-2475.2010.05.002

• 人工智能 • 上一篇    下一篇

基于联合决策树的客户流失预测模型设计

郭俊芳,周生宝   

  1. 山西大同大学数学与计算机科学学院,山西 大同 037009
  • 收稿日期:2010-01-25 修回日期:1900-01-01 出版日期:2010-05-10 发布日期:2010-05-10

Design of Customers Churn Prediction Model Based on Multi-classifier Class-combiner

GUO Jun-fang, ZHOU Sheng-bao   

  1. College of Mathematics and Computer Science, Shanxi Datong University, Datong 037009, China
  • Received:2010-01-25 Revised:1900-01-01 Online:2010-05-10 Published:2010-05-10

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。

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

Abstract: 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.

Key words: customers churn prediction, decision tree, multi-classifier class-combiner, data mining

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