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Reconciling performance and interpretability in customer churn prediction using ensemble learning based on generalized additive models

Koen De Bock (UGent) and Dirk Van den Poel (UGent)
(2012) EXPERT SYSTEMS WITH APPLICATIONS. 39(8). p.6816-6826
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SUPPORT VECTOR MACHINES, KNOWLEDGE DISCOVERY, RANDOM FORESTS, CLASSIFICATION, RETENTION, CLASSIFIERS, SATISFACTION, MANAGEMENT, DEFECTION, DIAGNOSIS, Database marketing, Customer churn prediction, Ensemble classification, Generalized additive models (GAMs), GAMens, Model interpretability

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Chicago
De Bock, Koen, and Dirk Van den Poel. 2012. “Reconciling Performance and Interpretability in Customer Churn Prediction Using Ensemble Learning Based on Generalized Additive Models.” Expert Systems with Applications 39 (8): 6816–6826.
APA
De Bock, K., & Van den Poel, D. (2012). Reconciling performance and interpretability in customer churn prediction using ensemble learning based on generalized additive models. EXPERT SYSTEMS WITH APPLICATIONS, 39(8), 6816–6826.
Vancouver
1.
De Bock K, Van den Poel D. Reconciling performance and interpretability in customer churn prediction using ensemble learning based on generalized additive models. EXPERT SYSTEMS WITH APPLICATIONS. 2012;39(8):6816–26.
MLA
De Bock, Koen, and Dirk Van den Poel. “Reconciling Performance and Interpretability in Customer Churn Prediction Using Ensemble Learning Based on Generalized Additive Models.” EXPERT SYSTEMS WITH APPLICATIONS 39.8 (2012): 6816–6826. Print.
@article{2086765,
  author       = {De Bock, Koen and Van den Poel, Dirk},
  issn         = {0957-4174},
  journal      = {EXPERT SYSTEMS WITH APPLICATIONS},
  keyword      = {SUPPORT VECTOR MACHINES,KNOWLEDGE DISCOVERY,RANDOM FORESTS,CLASSIFICATION,RETENTION,CLASSIFIERS,SATISFACTION,MANAGEMENT,DEFECTION,DIAGNOSIS,Database marketing,Customer churn prediction,Ensemble classification,Generalized additive models (GAMs),GAMens,Model interpretability},
  language     = {eng},
  number       = {8},
  pages        = {6816--6826},
  title        = {Reconciling performance and interpretability in customer churn prediction using ensemble learning based on generalized additive models},
  url          = {http://dx.doi.org/10.1016/j.eswa.2012.01.014},
  volume       = {39},
  year         = {2012},
}

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