Feature-selection-based dynamic transfer ensemble model for customer churn prediction-Reference-Cited by-同舟云学术

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Feature-selection-based dynamic transfer ensemble model for customer churn prediction

Published:2014-01-16 Issue:1 Volume:43 Page:29-51
ISSN:0219-1377
Container-title:Knowledge and Information Systems
language:en
Short-container-title:Knowl Inf Syst

Author:

Xiao Jin,Xiao Yi,Huang Anqiang,Liu Dunhu,Wang Shouyang

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence,Hardware and Architecture,Human-Computer Interaction,Information Systems,Software

Link

http://link.springer.com/content/pdf/10.1007/s10115-013-0722-y.pdf

Reference44 articles.

1. Dyché J (2001) The CRM handbook: a business guide to customer relationship management. Addison-Wesley, Reading

2. Bhattacharya CB (1998) When customers are members: customer retention in paid membership contexts. J Acad Market Sci 26(1):31–44

3. Neslin SA, Gupta S, Kamakura W, Lu JX, Mason CH (2006) Detection defection: measuring and understanding the predictive accuracy of customer churn models. J Market Res 43(2):204–211

4. Au W, Chan KCC, Yao X (2004) A novel evolutionary data mining algorithm with applications to churn prediction. IEEE T Evol Comput 7(6):532–545

5. Kisioglu P, Topcu YI (2011) Applying Bayesian belief network approach to customer churn analysis: a case study on the telecom industry of Turkey. Expert Syst Appl 38(6):7151–7157

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