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The relevant length of customer event history for churn prediction : how long is long enough?
Ballings, Michel; Van den Poel, Dirk - 2012
Persistent link: https://www.econbiz.de/10009665091
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Kernel factory : an ensemble of kernel machines
Ballings, Michel; Van den Poel, Dirk - 2012
Persistent link: https://www.econbiz.de/10009713367
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Ensemble classification based on generalized additive models
De Bock, Koen W.; Coussement, Kristof; Van den Poel, Dirk - 2009
Persistent link: https://www.econbiz.de/10003936509
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Mining ideas from textual information
Thorleuchter, Dirk; Van den Poel, Dirk - 2009
Persistent link: https://www.econbiz.de/10003936689
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Improving customer complaint management by automatic email classification using linguistic style features as predictors
Coussement, Kristof (contributor);  … - 2007
Persistent link: https://www.econbiz.de/10003581336
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An empirical evaluation of rotation-based ensemble classifiers for customer churn prediction
De Bock, Koen W.; Van den Poel, Dirk - 2011
Persistent link: https://www.econbiz.de/10009240156
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Technology classification with latent semantic indexing
Thorleuchter, Dirk; Van den Poel, Dirk - 2012
Persistent link: https://www.econbiz.de/10009664937
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Predicting partial customer churn using Markov for discrimination for modeling first purchase sequences
Miguéis, Vera L.; Van den Poel, Dirk; Camanho, A. S.; … - 2012
Persistent link: https://www.econbiz.de/10009665058
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Reconciling performance and interpretability in customer churn prediction using ensemble learning based on generalized additive models
De Bock, Koen W.; Van den Poel, Dirk - 2012
Persistent link: https://www.econbiz.de/10009665069