An efficient noise-filtered ensemble model for customer churn analysis in aviation industry

2019 ◽  
Vol 37 (2) ◽  
pp. 2575-2585
Author(s):  
Yongjun Li ◽  
Jianshuang Wei ◽  
Kai Kang ◽  
Zhouyang Wu
2014 ◽  
Vol 43 (1) ◽  
pp. 29-51 ◽  
Author(s):  
Jin Xiao ◽  
Yi Xiao ◽  
Anqiang Huang ◽  
Dunhu Liu ◽  
Shouyang Wang

2014 ◽  
Vol 644-650 ◽  
pp. 2198-2201
Author(s):  
Li Chang Zhen ◽  
Xin Gao ◽  
Yi Ming Wang ◽  
Yong Chun Gao

With the further reform and market division in the telecommunication industry, there are more and more choices for customers to select telecom products and operators, which lead to the fiercer competition for customers between telecom operators. As the technical method to identify customers churn, the data mining can help the telecom competitors to analyze some seemingly unrelated data into meaningful information. On the basis of the research on the vital problems in the telecom companies, this paper explains how to apply data mining techniques to customer churn analysis, proposes the specific procedures and technology solutions to prevent the customer churn and builds the models of the data mining by analyzing the related algorithm. Finally, based on the systematical analysis on theory and method to data mining, the paper draws the conclusion that the customers churn listing and tree algorithm can solve the practical problems of the customer churn in telecommunication industry.


2014 ◽  
Vol 2014 ◽  
pp. 1-15
Author(s):  
Jin Xiao ◽  
Bing Zhu ◽  
Geer Teng ◽  
Changzheng He ◽  
Dunhu Liu

Scientific customer value segmentation (CVS) is the base of efficient customer relationship management, and customer credit scoring, fraud detection, and churn prediction all belong to CVS. In real CVS, the customer data usually include lots of missing values, which may affect the performance of CVS model greatly. This study proposes a one-step dynamic classifier ensemble model for missing values (ODCEM) model. On the one hand, ODCEM integrates the preprocess of missing values and the classification modeling into one step; on the other hand, it utilizes multiple classifiers ensemble technology in constructing the classification models. The empirical results in credit scoring dataset “German” from UCI and the real customer churn prediction dataset “China churn” show that the ODCEM outperforms four commonly used “two-step” models and the ensemble based model LMF and can provide better decision support for market managers.


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