Customer churn prediction using improved balanced random forests

[1]  Kristof Coussement,et al.  Faculteit Economie En Bedrijfskunde Hoveniersberg 24 B-9000 Gent Churn Prediction in Subscription Services: an Application of Support Vector Machines While Comparing Two Parameter-selection Techniques Churn Prediction in Subscription Services: an Application of Support Vector Machines While Comparin , 2022 .

[2]  Dirk Van den Poel,et al.  CRM at a pay-TV company: Using analytical models to reduce customer attrition by targeted marketing for subscription services , 2007, Expert Syst. Appl..

[3]  Andy Liaw,et al.  Classification and Regression by randomForest , 2007 .

[4]  T. Yalcinoz,et al.  Implementing soft computing techniques to solve economic dispatch problem in power systems , 2008, Expert Syst. Appl..

[5]  Wagner A. Kamakura,et al.  Defection Detection: Measuring and Understanding the Predictive Accuracy of Customer Churn Models , 2006 .

[6]  Christophe Croux,et al.  Bagging and Boosting Classification Trees to Predict Churn , 2006 .

[7]  Dirk Van den Poel,et al.  Predicting customer retention and profitability by using random forests and regression forests techniques , 2005, Expert Syst. Appl..

[8]  Yu Zhao,et al.  Customer Churn Prediction Using Improved One-Class Support Vector Machine , 2005, ADMA.

[9]  Dirk Van den Poel,et al.  Customer base analysis: partial defection of behaviourally loyal clients in a non-contractual FMCG retail setting , 2005, Eur. J. Oper. Res..

[10]  Dirk Van den Poel,et al.  Investigating the role of product features in preventing customer churn, by using survival analysis and choice modeling: The case of financial services , 2004, Expert Syst. Appl..

[11]  Leo Breiman,et al.  Random Forests , 2001, Machine Learning.

[12]  Mu Zhichun.  Bayesian Network Classifier and Its Application in CRM , 2004 .

[13]  Chao Chen,et al.  Using Random Forest to Learn Imbalanced Data , 2004 .

[14]  S. Neslin.  Defection Detection : Improving Predictive Accuracy of Customer Churn Models , 2004 .

[15]  Xin Yao,et al.  A novel evolutionary data mining algorithm with applications to churn prediction , 2003, IEEE Trans. Evol. Comput..

[16]  Cheng-Jung Lin,et al.  Goal-oriented sequential pattern for network banking churn analysis , 2003, Expert Syst. Appl..

[17]  Charles Elkan,et al.  The Foundations of Cost-Sensitive Learning , 2001, IJCAI.

[18]  Bernhard Schölkopf,et al.  Estimating the Support of a High-Dimensional Distribution , 2001, Neural Computation.

[19]  Eric Johnson,et al.  Predicting subscriber dissatisfaction and improving retention in the wireless telecommunications industry , 2000, IEEE Trans. Neural Networks Learn. Syst..

[20]  Eric Johnson,et al.  Churn Reduction in the Wireless Industry , 1999, NIPS.

[21]  A. E. Eiben,et al.  Genetic Modelling of Customer Retention , 1998, EuroGP.

[22]  Z. Degraeve,et al.  The attrition of volunteers , 1997 .

[23]  R. Rust,et al.  Mathematical models of service , 1996 .