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Published Papers in Refereed Journals	°K.W. De Bock, K. Coussement and D. Van den Poel, Ensemble Classification Based on Generalized Additive Models, Computational Statistics & Data Analysis, forthcoming (2010) °K. Coussement, D.F. Benoit and D. Van den Poel, Improved Marketing Decision Making in a Customer Churn Prediction Context Using Generalized Additive Models, Expert Systems with Applications, 37 (2010), pp 2132-2143. °K. Coussement and D. Van den Poel, Improving Customer Attrition Prediction by Integrating Emotions from Client/Company Interaction Emails and Evaluating Multiple Classifiers, Expert Systems with Applications, 36 (3) (2009), pp 6127-6134. °K. Coussement and D. Van den Poel, Integrating the voice of customers through call center emails into a decision support system for churn prediction, Information and Management, 45 (3) (2008). °K. Coussement and D. Van den Poel, Improving Customer Complaint Management by Automatic Email Classification Using Linguistic Style Features as Predictors, Decision Support Systems, 44 (4) (2008), pp 370-382. °K. Coussement and D. Van den Poel, Churn prediction in subscription services: an application of support vector machines while comparing two parameter-selection techniques, Expert systems with applications, 34 (1) (2008), pp 313-327.
Communications in Refereed Conferences	°I. Ajili, K. Coussement and M. Limam, Effect of Incorporating Data Quality Matrices on Classification Mining. In Proceedings of Meeting on Statistics & Data Mining (short paper), Hammamet (Tunisia), March 11-12 (2010). °K. Coussement, Customer Intelligence: Tapping the Vein of your Customers. In Proceedings of Meeting on Statistics & Data Mining (short paper), Hammamet (Tunisia), March 11-12 (2010). °K. Coussement and D. Van den Poel, Improving Customer Complaint Management by Automatic Email Classification Using Linguistic Style Features In Proceedings of Marketing Science (abstract), Vancouver (Canada), June 12-14 (2008). °K. Coussement, Employing SAS® Text Miner Methodology to Become a Customer Genius in Customer Churn Prediction Complaint E-mail Management. In Proceedings of SAS Global Forum 2008 (short paper – invited talk), San Antonio (Texas, USA), March 16-18 (2008). °K. Coussement and D. Van den Poel, Combining Unstructured/Structured information into a traditional churn prediction model. In Proceedings of ISSPR 2007 (poster), Plymouth (UK), July 22-27 (2007). °K. Coussement and D. Van den Poel, Integrating the voice of customers through call center emails into a churn predictions system. In Proceedings of Marketing Science (abstract), Singapore (Singapore), June 28-30 (2007).
Other Conference and Seminar Presentations                               Working Papers from Universities and Similar Institutions                                                                               Reseach Papers         Books           Software	°K. Coussement, Text Mining and Customer Intelligence: Their Marriage Untangled!, SAS Forum France 2009 (invited talk), Paris (France), October 21-22 (2009). °K.W. De Bock, K. Coussement and D. Van den Poel, GAMbag, GAMrfs and GAM Forest: Three Ensemble Classifiers Based on Generalized Additive Models, Faculty of Economics and Business Administration PhD Day (poster session), Ghent University, May 25 (2009). °K. Coussement, GAMbag, GAMrfs and GAM Forest: Three Ensemble Classifiers Based on Generalized Additive Models (based on K.W. De Bock, K. Coussement and D. Van den Poel, Ensemble Classification Based on Generalized Additive Models, in Computational Statistics and Data Analysis), University College Brussels, April 28 (2009). °K. Coussement, Customer Intelligence Untangled, IÉSEG School of Management, February 26 (2009).   °K.W. De Bock, K. Coussement and D. Van den Poel, Ensemble Classification Based on Generalized Additive Models, HUB Research paper January 2010/02, p 30. °K.W. De Bock, K. Coussement and D. Van den Poel, Ensemble Classification Based on Generalized Additive Models, IESEG Research paper (2010-MAN-01) January 2010, p 30. °K.W. De Bock, K. Coussement and D. Van den Poel, Ensemble Classification Based on Generalized Additive Models, LEM Research paper (2010-02) January 2010, p 30. °K.W. De Bock, K. Coussement and D. Van den Poel, Ensemble Classification Based on Generalized Additive Models, UGent working paper series December 2009, p 30. °K. Coussement, D.F. Benoit and D. Van den Poel, Improved Marketing Decision Making in a Customer Churn Prediction Context Using Generalized Additive Models, HUB Research paper August 2009/18, p 35. °K. Coussement, D.F. Benoit and D. Van den Poel, Improved Marketing Decision Making in a Customer Churn Prediction Context Using Generalized Additive Models, UGent Working paper series July 2009, p 34. °K. Coussement and D. Van den Poel, Improving Customer Attrition Prediction by Integrating Emotions from Client/Company Interaction Emails and Evaluating Multiple Classifiers, UGent Working paper series July 2008, p 32. °K. Coussement and D. Van den Poel, Integrating the voice of customers through call center emails into a decision support system for churn prediction, UGent Working paper series February 2008, p 27. °K. Coussement and D. Van den Poel, Improving Customer Complaint Management by Automatic Email Classification Using Linguistic Style Features as Predictors, UGent Working paper series September 2007, p 35. °K. Coussement and D. Van den Poel, Churn prediction in subscription services: an application of support vector machines while comparing two parameter-selection techniques, UGent Working paper series September 2006, p 55.   °K.W. De Bock, K. Coussement and D. Van den Poel, GAMens: Applies GAMbag, GAMrsm and GAMens ensemble classifiers for binary classification, R Reference Manual version 1.1 (2010).   °K. Coussement (2008), Issues in Customer Intelligence: Data and Method Creativity to Improve Marketing Decision Making, Doctoral Dissertation submitted to the Faculty of Economics & Business Administration, Ghent University (Belgium)   °K.W. De Bock, K. Coussement and D. Van den Poel, GAMens: Applies GAMbag, GAMrsm and GAMens ensemble classifiers for binary classification, R Package 1.1 (2010).

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