Using Genetic K-Means Algorithm for PC A Regression Data in Customer Churn Prediction

摘要：

Imbalance distribution of samples between churners and non-churners can hugely affect churn prediction results in telecommunication services field. One method to solve this is over-sampling approach by PCA regression. However, PCA regression may not generate good churn samples if a dataset is nonlinear discriminant. We employed Genetic K-means Algorithm to cluster a dataset to find locally optimum small dataset to overcome the problem. The experiments were carried out on a real-world telecommunication dataset and assessed on a churn prediction task. The experiments showed that Genetic K-means Algorithm can improve prediction results for PCA regression and performed as good as SMOTE.

关键词： Genetic K-means Algorithm PCA Regression Nonlinear Discriminant Churn Prediction Imbalanced Distribution of Classes

作者: Bingquan Huang T. Satoh Y. Huang M.-T. Kechadi B. Buckley

作者单位: School of Computer Science and Informatics, University College Dublin,Belfield, Dublin 4, 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: 210-220

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）