A Decreasing Binary Decision Tree Classification for Unbalanced Data in Customer Value Segmentation

摘要：

The traditional data mining can not reflect the influence caused by the unbalanced quantity distribution of customers who have a variety of values.To solve the bias learning caused by unbalanced data in customer value segmentation,this paper proposes a classifying method based on decreasing binary decision tree,which transforms a multi-classification to a series of binary classifying trees.In every step of the method,the quantitative difference between the binary classes is decreased continually by removing the samples which are classified at the highest accuracy,and further classification only focused on the rest samples.The test result proves that this method can optimize the accuracy of all customer sample classes and produce effective rules for multi-classification in customer value segmentation.

关键词： Customer value segmentation Decision tree Unbalanced data

作者: Peng ZOU Jiahui MO Yijun LI

作者单位: School of Management,Harbin Institute of Technology,P.R.China,150001

会议类型: 国际会议

会议名称: The 2008 International Conference on Business Intelligence and Financial Engineering(BIFE 2008)(商业智能和金融工程国际会议)

会议地点: 长沙

会议语种:英文

页码: 317-322

在线出版日期: 2008-10-28（万方平台首次上网日期，不代表论文的发表时间）