Key-Attributes-Based Ensemble Classifier for Customer Churn Prediction

作     者：Yu Qian Liang-Qiang Li Jian-Rong Ran Pei-Ji Shao 

作者机构：School of Management and EconomicsUniversity of Electronic Science and Technology of ChinaChengdu 611731 

出 版 物：《Journal of Electronic Science and Technology》 (电子科技学刊（英文版）)

年 卷 期：2018年第16卷第1期

页      面：37-44页

核心收录：

学科分类：070208[理学-无线电物理] 07[理学] 0702[理学-物理学] 

基　　金：supported by the National Natural Science Foundation of China under Grants No.71271044 and 71572029 

主　　题：Customer churn data mining ensemble classifier key attribute 

摘      要：Recently, it has been seen that the ensemble classifier is an effective way to enhance the prediction performance. However, it usually suffers from the problem of how to construct an appropriate classifier based on a set of complex data, for example,the data with many dimensions or hierarchical attributes. This study proposes a method to constructe an ensemble classifier based on the key attributes. In addition to its high-performance on precision shared by common ensemble classifiers, the calculation results are highly intelligible and thus easy for ***, the experimental results based on the real data collected from China Mobile show that the keyattributes-based ensemble classifier has the good performance on both of the classifier construction and the customer churn prediction.