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Ensembles of probability estimation trees for customer churn prediction

Koen De Bock (UGent) and Dirk Van den Poel (UGent)
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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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Chicago
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. Nicolás García-Pedrajas, Francisco Herrera, Colin Fyfe, José Manuel Benítez, and Moonis Ali, 6097:57–66. Berlin, Germany: Springer.
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). Presented at the 23rd International conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems (IEA/AIE 2010), Berlin, Germany: Springer.
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.
MLA
De Bock, Koen, and Dirk Van den Poel. “Ensembles of Probability Estimation Trees for Customer Churn Prediction.” Lecture Notes in Computer Science. Ed. Nicolás García-Pedrajas et al. Vol. 6097. Berlin, Germany: Springer, 2010. 57–66. Print.
@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       = {Garc{\'i}a-Pedrajas, Nicol{\'a}s and Herrera, Francisco and Fyfe, Colin and Ben{\'i}tez, Jos{\'e} Manuel and Ali, Moonis},
  isbn         = {9783642130243},
  issn         = {0302-9743},
  keyword      = {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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