Churn prediction with Linear Discriminant Boosting algorithm

Proceedings of the 7th International Conference on Machine Learning and Cybernetics, ICMLC

Volumn 1, Issue , 2008, Pages 228-233

  Xie, Yaya ^a   Li, Xiu ^a  

^a TSINGHUA UNIVERSITY   (China)

Author keywords

Boosting; Churn prediction; Linear discriminant analysis

Indexed keywords

CONTROL THEORY; CYBERNETICS; DECISION THEORY; DECISION TREES; DISCRIMINANT ANALYSIS; EDUCATION; FACE RECOGNITION; FORECASTING; IMAGE RETRIEVAL; LADDER NETWORKS; LEARNING SYSTEMS; MATHEMATICAL MODELS; MULTILAYER NEURAL NETWORKS; NEURAL NETWORKS; ROBOT LEARNING; SUPPORT VECTOR MACHINES; TREES (MATHEMATICS);

ADABOOST; ARTIFICIAL NEURAL NETWORKS; BINARY CLASSIFICATION PROBLEMS; BOOSTING; BOOSTING ALGORITHMS; CHURN PREDICTION; CLASSIFICATION ALGORITHMS; CLOSED FORMS; COMPUTATIONAL SIMPLICITIES; CUSTOMER CHURN DATA SETS; EXHAUSTIVE SEARCHES; LINEAR DISCRIMINANT ANALYSIS; MISCLASSIFICATION; NUMERICAL OPTIMIZATIONS; OTHER ALGORITHMS; PREDICTION ACCURACIES; SUPPORT VECTORS; TRAINING SAMPLES;

LEARNING ALGORITHMS;

EID: 57849125023     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICMLC.2008.4620409     Document Type: Conference Paper

References (20)

1. M. Geib, A. Reichold, L. Kolbe, W. Brenner, "Architecture for Customer Relationship Management Approaches in Financial Services", Proceedings of the 38th Hawaii International Conference on System Sciences, pp. 240, 2005.
2. W. Reinartz, & V. Kumar, "The impact of customer relationship characteristics on profitable lifetime duration", Journal of Marketing, Vol. 67, No. 1, pp. 77-99, 2003.
3. L. Drysdale, "Relationships Marketing: a new paradigm for marketing of schools", The Official Journal of Secondary Principals, pp. 36-37, 1999.
4. R. Javalgi, T. Whipple, A. Ghosh, R. Young, "Market orientation, strategic flexibility, and performance: implications for services providers", Journal of Services Marketing, Vol. 19, No. 4, pp. 212-221, 1997.
5. Neslin, S., Gupta, S., Kamakura, W., Lu, J., Mason, C., "Defection detection: improving predictive accuracy of customer churn models", Journal of Marketing Research, 2006.
6. M. C. Mozer, R. Wolniewicz, D. B. Grimes, E. Johnson, H. Kaushansky, "Predicting Subscriber Dissatisfaction and Improving Retention in the Wireless Telecommunication Industry", IEEE Transactions on Neural Networks, Specialissue on Data Mining and Knowledge Representation, Vol. 11, No. 3, pp. 690-696, 2000.
7. Y. Zhao, B. Li, X. Li, W. Liu, S. Ren, "Customer churn prediction using improved one-class support vector machine", Lecture Notes in Computer Science, 3584, pp. 300-306, 2005.
8. M. C. Mozer, R. Wolniewicz, D. B. Grimes, "Churn reduction in the wireless industry", Advances in Neural Information Processing Systems, Vol. 12, pp. 935-941, 2000.
9. T. Mutanen, J. Ahola, S. Nousiainen, "Customer churn prediction - a case study in retail banking", ECML PKDD 2006 Workshop on Practical Data Mining: Applications, Experiences and Challenges, 2006.
10. B. Luo, P. Shao, J. Liu, "Customer Churn Prediction Based on the Decision Tree in Personal Handyphone System Service", 2007 International Conference on Service Systems and Service Management, pp. 1-5, 2007.
11. D. Chiang, Y. Wang, S. Lee, C. Lin, "Goal-oriented sequential pattern for network banking churn analysis", Expert Systems with Applications, Vol. 25, No. 3, pp. 293-302, 2003.
12. A. E. Eiben, A. E. Koudijs, F. Slisser, "Genetic modelling of customer retention", Lecture Notes in Computer Science, 1391, 178-186, 1998.
13. WH. Au, K. Chan, X. Yao, "A novel evolutionary data mining algorithm with applications to churn prediction", IEEE Transactions on Evolutionary Computation, Vol. 7, No. 6, 532-545, 2003.
14. N. Luo, Z. MU, "Bayesian network classifier and its application in CRM", Computer Application, Vol. 24, No. 3, pp. 79-81, 2004.
15. B. Lariviere, D. Van den Poel, "Investigating the role of product features in preventing customer chum by using survival analysis and choice modeling: The case of financial services", Expert Systems with Applications, Vol. 27, pp. 277-285, 2004.
16. Y. Freund, R. E. Shapiro, "Experiments with a new boosting algorithm", Proceedings of the Thirteenth International Conference on Machine Learning, Morgan-Kaufman, SanFrancisco, CA, pp. 148-156, 1996.
17. S. Balakrishnama, A. Ganapathiraju, J. Picone, "Linear discriminant analysis for signal processing problems", Southeastcon'99, pp. 78-81, 1999.
18. R. Schapire, Y. Singer, "Improved Boosting Algorithms Using Confidence-rated Predictions", Machine Learning, Vol. 37, pp. 297-336, 1999.
19. J. Shao, X. Li, W. Liu, "The Application of AdaBoost in Customer Churn Prediction", 2007 International Conference on Service Systems and Service Management, pp. 1-6, 2007.
20. S. M. Weiss & C. A. Kulikowski, Computer systems that learns Classification and prediction methods from statistics, neural nets", machine learning, and expert systems, Los Atlos, CA: Morgan Kaufmann, 1991.