Using PCA to Predict Customer Churn in Telecommunication Dataset

摘要：

Failure to identify potential churners affects significantly a company revenues and services that can provide. Imbalance distribution of instances between churners and non-churners and the size of customer dataset are the concerns when building a churn prediction model. This paper presents a local PCA classifier approach to avoid these problems by comparing eigenvalues of the best principal component. The experiments were carried out on a large real-world Telecommunication dataset and assessed on a churn prediction task. The experimental results showed that local PCA classifier generally outperformed Naive Bayes, Logistic regression, SVM and Decision Tree C4.5 in terms of true churn rate.

关键词： PCA predict potential churners telecommunication dataset

作者: T. Sato B.Q. Huang Y. Huang M.-T. Kechadi B. Buckley

作者单位: School of Computer Science and Informatics, University College Dublin, Belfield,Dublin 4, Ireland Eircom Limited, 1 Heuston South Quarter, Dublin 8, Ireland

会议类型: 国际会议

会议名称: 6th International Conference on Advanced Data Mining and Applications(第六届先进数据挖掘及应用国际会议 ADMA 2010)

会议地点: 重庆

会议语种:英文

页码: 326-335

在线出版日期: 2010-11-19（万方平台首次上网日期，不代表论文的发表时间）