Ensembles of probability estimation trees for customer churn prediction
- Author
- Koen De Bock (UGent) and Dirk Van den Poel (UGent)
- Organization
- Abstract
- Customer churn prediction is one of the most, important elements tents of a company's Customer Relationship Management, (CRM) strategy In tins study, two strategies are investigated to increase the lift. performance of ensemble classification models, i.e (1) using probability estimation trees (PETs) instead of standard decision trees as base classifiers; and (n) implementing alternative fusion rules based on lift weights lot the combination of ensemble member's outputs Experiments ale conducted lot font popular ensemble strategics on five real-life chin n data sets In general, the results demonstrate how lift performance can be substantially improved by using alternative base classifiers and fusion tides However: the effect vanes lot the (Idol cut ensemble strategies lit particular, the results indicate an increase of lift performance of (1) Bagging by implementing C4 4 base classifiets. (n) the Random Subspace Method (RSM) by using lift-weighted fusion rules, and (in) AdaBoost, by implementing both.
- Keywords
- lift, RANKING, FORESTS, database marketing, probability estimation trees, churn prediction, PETs, ensemble classification, CRM
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Citation
Please use this url to cite or link to this publication: http://hdl.handle.net/1854/LU-1230867
- MLA
- De Bock, Koen, and Dirk Van den Poel. “Ensembles of Probability Estimation Trees for Customer Churn Prediction.” LECTURE NOTES IN COMPUTER SCIENCE, edited by Nicolás García-Pedrajas et al., vol. 6097, Springer, 2010, pp. 57–66, doi:10.1007/978-3-642-13025-0_7.
- APA
- De Bock, K., & Van den Poel, D. (2010). Ensembles of probability estimation trees for customer churn prediction. In N. García-Pedrajas, F. Herrera, C. Fyfe, J. M. Benítez, & M. Ali (Eds.), LECTURE NOTES IN COMPUTER SCIENCE (Vol. 6097, pp. 57–66). https://doi.org/10.1007/978-3-642-13025-0_7
- Chicago author-date
- De Bock, Koen, and Dirk Van den Poel. 2010. “Ensembles of Probability Estimation Trees for Customer Churn Prediction.” In LECTURE NOTES IN COMPUTER SCIENCE, edited by Nicolás García-Pedrajas, Francisco Herrera, Colin Fyfe, José Manuel Benítez, and Moonis Ali, 6097:57–66. Berlin, Germany: Springer. https://doi.org/10.1007/978-3-642-13025-0_7.
- Chicago author-date (all authors)
- De Bock, Koen, and Dirk Van den Poel. 2010. “Ensembles of Probability Estimation Trees for Customer Churn Prediction.” In LECTURE NOTES IN COMPUTER SCIENCE, ed by. Nicolás García-Pedrajas, Francisco Herrera, Colin Fyfe, José Manuel Benítez, and Moonis Ali, 6097:57–66. Berlin, Germany: Springer. doi:10.1007/978-3-642-13025-0_7.
- Vancouver
- 1.De Bock K, Van den Poel D. Ensembles of probability estimation trees for customer churn prediction. In: García-Pedrajas N, Herrera F, Fyfe C, Benítez JM, Ali M, editors. LECTURE NOTES IN COMPUTER SCIENCE. Berlin, Germany: Springer; 2010. p. 57–66.
- IEEE
- [1]K. De Bock and D. Van den Poel, “Ensembles of probability estimation trees for customer churn prediction,” in LECTURE NOTES IN COMPUTER SCIENCE, Cordoba, Spain, 2010, vol. 6097, pp. 57–66.
@inproceedings{1230867,
abstract = {{Customer churn prediction is one of the most, important elements tents of a company's Customer Relationship Management, (CRM) strategy In tins study, two strategies are investigated to increase the lift. performance of ensemble classification models, i.e (1) using probability estimation trees (PETs) instead of standard decision trees as base classifiers; and (n) implementing alternative fusion rules based on lift weights lot the combination of ensemble member's outputs Experiments ale conducted lot font popular ensemble strategics on five real-life chin n data sets In general, the results demonstrate how lift performance can be substantially improved by using alternative base classifiers and fusion tides However: the effect vanes lot the (Idol cut ensemble strategies lit particular, the results indicate an increase of lift performance of (1) Bagging by implementing C4 4 base classifiets. (n) the Random Subspace Method (RSM) by using lift-weighted fusion rules, and (in) AdaBoost, by implementing both.}},
author = {{De Bock, Koen and Van den Poel, Dirk}},
booktitle = {{LECTURE NOTES IN COMPUTER SCIENCE}},
editor = {{García-Pedrajas, Nicolás and Herrera, Francisco and Fyfe, Colin and Benítez, José Manuel and Ali, Moonis}},
isbn = {{9783642130243}},
issn = {{0302-9743}},
keywords = {{lift,RANKING,FORESTS,database marketing,probability estimation trees,churn prediction,PETs,ensemble classification,CRM}},
language = {{eng}},
location = {{Cordoba, Spain}},
pages = {{57--66}},
publisher = {{Springer}},
title = {{Ensembles of probability estimation trees for customer churn prediction}},
url = {{http://dx.doi.org/10.1007/978-3-642-13025-0_7}},
volume = {{6097}},
year = {{2010}},
}
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