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Ensembles of probability estimation trees for customer churn prediction

Koen De Bock UGent and Dirk Van den Poel UGent (2010) LECTURE NOTES IN COMPUTER SCIENCE. 6097. p.57-66
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.
Please use this url to cite or link to this publication:
author
organization
year
type
conference (proceedingsPaper)
publication status
published
subject
keyword
lift, RANKING, FORESTS, database marketing, probability estimation trees, churn prediction, PETs, ensemble classification, CRM
in
LECTURE NOTES IN COMPUTER SCIENCE
Lect. Notes Comput. Sci.
editor
Nicolás García-Pedrajas, Francisco Herrera, Colin Fyfe, José Manuel Benítez and Moonis Ali
volume
6097
issue title
Trends in applied intelligent systems, pt II, proceedings
pages
57 - 66
publisher
Springer
place of publication
Berlin, Germany
conference name
23rd International conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems (IEA/AIE 2010)
conference location
Cordoba, Spain
conference start
2010-06-01
conference end
2010-06-04
Web of Science type
Proceedings Paper
Web of Science id
000281604400007
ISSN
0302-9743
ISBN
9783642130243
DOI
10.1007/978-3-642-13025-0_7
language
English
UGent publication?
yes
classification
P1
copyright statement
I have transferred the copyright for this publication to the publisher
id
1230867
handle
http://hdl.handle.net/1854/LU-1230867
date created
2011-05-23 16:25:15
date last changed
2017-01-02 09:52:34
@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},
}

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.