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Building comprehensible customer churn prediction models with advanced rule induction techniques

Verbeke, Wouter, Martens, David, Mues, Christophe, Baesens, Bart·2011·Expert Systems with Applications
DOI 10.1016/j.eswa.2010.08.023
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Research works printed but not linked: 45

  1. Agrawal, R., & Srikant, R. (1994). Fast algorithms for mining association rules in large databases. In VLDB (pp. 487–499).
  2. Athanassopoulos, 2000, Customer satisfaction cues to support market segmentation and explain switching behavior, Journal of Business Research, 47, 191, 10.1016/S0148-2963(98)00060-5 · DOI · Paper page
  3. Baesens, 2003, Benchmarking state-of-the-art classification algorithms for credit scoring, Journal of the Operational Research Society, 54, 627, 10.1057/palgrave.jors.2601545 · DOI · Paper page
  4. Bhattacharya, 1998, When customers are members: Customer retention in paid membership contexts, Journal of the Academy of Marketing Science, 26, 31, 10.1177/0092070398261004 · DOI · Paper page
  5. Blum, 2005, Beam-ACO – hybridizing ant colony optimization with beam search: An application to open shop scheduling, Computers & Operations Research, 32, 1565, 10.1016/j.cor.2003.11.018 · DOI · Paper page
  6. Boryczka, 2009, Finding groups in data: Cluster analysis with ants, Applied Soft Computing, 9, 61, 10.1016/j.asoc.2008.03.002 · doi:10.1016/j.asoc.2008.03.002
  7. Buckinx, 2005, Customer base analysis: Partial defection of behaviourally loyal clients in a non-contractual FMCG retail setting, European Journal Of Operational Research, 164, 252, 10.1016/j.ejor.2003.12.010 · DOI · Paper page
  8. Bullnheimer, B., Hartl, R., & Strauss, C. (1999). Applying the ant system to the vehicle routing problem. In Voss, S., Martello, S., Osman, I., Roucairol, C. (Eds.), Meta-Heuristics: Advances and trends in local search paradigms for optimization.
  9. Burez, 2009, Handling class imbalance in customer churn prediction, Expert Systems with Applications, 36, 4626, 10.1016/j.eswa.2008.05.027 · DOI · Paper page
  10. Cohn, 1994, Improving generalization with active learning, Machine Learning, 15, 201, 10.1007/BF00993277 · DOI · Paper page
  11. Colgate, 2000, Implementing a customer relationship strategy: The assymetric impact of poor versus excellent execution, Journal of the Academy of Marketing Science, 28, 375, 10.1177/0092070300283006 · DOI · Paper page
  12. Colgate, 1996, Customer defection: A study of the student market in ireland, International Journal of Bank Marketing, 14, 23, 10.1108/02652329610113144 · DOI · Paper page
  13. Colorni, 1994, Ant system for jobÜshop scheduling, Journal of Operations Research, Statistics and Computer Science, 34, 39
  14. Coussement, 2008, Churn prediction in subscription services: An application of support vector machines while comparing two parameter-selection techniques, Expert Systems with Applications, 34, 313, 10.1016/j.eswa.2006.09.038 · DOI · Paper page
  15. Cumps, 2009, Inferring rules for business/ict alignment using ants, Information and Management, 46, 116, 10.1016/j.im.2008.05.005 · DOI
  16. Datta, 2000, Automated cellular modeling and prediction on a large scale, Artificial Intelligence Review, 14, 485, 10.1023/A:1006643109702 · DOI
  17. Dietterich, 1998, Approximate statistical test for comparing supervised classification learning algorithms, Neural Computation, 10, 1895, 10.1162/089976698300017197 · DOI · Paper page
  18. Dorigo, 1996, Ant System: Optimization by a colony of cooperating agents, IEEE Transactions on Systems, Man, and Cybernetics Part B: Cybernetics, 26, 29, 10.1109/3477.484436 · DOI · Paper page
  19. Eiben, 1998, Genetic modeling of customer retention, Lecture Notes in Computer Science, 1391, 178, 10.1007/BFb0055937 · DOI
  20. Garcia, 2009, Path planning for autonomous mobile robot navigation with ant colony optimization and fuzzy cost function evaluation, Applied Soft Computing, 9, 1102, 10.1016/j.asoc.2009.02.014 · DOI · Paper page
  21. Glady, 2009, A modified pareto/NBD approach for predicting customer lifetime value, Expert Systems with Applications, 36, 2062, 10.1016/j.eswa.2007.12.049 · DOI
  22. Handl, 2006, Ant-based clustering and topographic mapping, Artificial Life, 12, 35, 10.1162/106454606775186400 · DOI · Paper page
  23. Hung, 2006, Applying data mining to telecom churn management, Expert Systems with Applications, 31, 515, 10.1016/j.eswa.2005.09.080 · DOI · Paper page
  24. Hwang, 2004, An LTV model and customer segmentation based on customer value: a case study on the wireless telecommunication industry, Expert Systems with Applications, 26, 181, 10.1016/S0957-4174(03)00133-7 · DOI · Paper page
  25. Kumar, 2008, Predicting credit card customer churn in banks using data mining, International Journal of Data Analysis Techniques and Strategies, 1, 4, 10.1504/IJDATS.2008.020020 · DOI
  26. Larivière, 2005, Predicting customer retention and profitability by using random forest and regression forest techniques, Expert Systems with Applications, 29, 472, 10.1016/j.eswa.2005.04.043 · DOI · Paper page
  27. Lima, 2009, Domain knowledge integration in data mining using decision tables: Case studies in churn prediction, Journal of the Operational Research Society, 60, 1096, 10.1057/jors.2008.161 · DOI
  28. Madden, 1999, Subscriber churn in the Australian ISP market, Information Economics and Policy, 10.1016/S0167-6245(99)00015-3 · DOI · Paper page
  29. Martens, 2008, Predicting going concern opinion with data mining, Decision Support Systems, 45, 765, 10.1016/j.dss.2008.01.003 · DOI
  30. Martens, 2006, Ant-based approach to the knowledge fusion problem, Vol. 4150, 84
  31. Martens, 2007, Classification with ant colony optimization, IEEE Transaction on Evolutionary Computation, 11, 651, 10.1109/TEVC.2006.890229 · DOI · Paper page
  32. Martens, 2009, Decompositional rule extraction from support vector machines by active learning, IEEE Transactions on Knowledge and Data Engineering, 21, 178, 10.1109/TKDE.2008.131 · DOI · Paper page
  33. Mizerski, 1982, An attribution explanation of the disproportionate influence of unfavourable information, Journal of Consumer Research, 9, 301, 10.1086/208925 · DOI · Paper page
  34. Mozer, 2000, Predicting subscriber dissatisfaction and improving retention in the wireless telecommunications industry, IEEE Transactions on Neural Networks, 11, 690, 10.1109/72.846740 · DOI · Paper page
  35. Paulin, 1998, Relational norms and client retention: External effectiveness of commercial banking in canada and mexico, International Journal of Bank Marketing, 16, 24, 10.1108/02652329810197807 · DOI
  36. Rasmusson, 1999, Complaints can build relationships, Sales and Marketing Management, 151, 89
  37. Reichheld, 1996, Learning from customer defections, Harvard Business Review, 74, 56
  38. Rust, 1993, Customer satisfaction, customer retention, and market share, Journal of Retailing, 69, 193, 10.1016/0022-4359(93)90003-2 · DOI · Paper page
  39. Stum, 1991, Building customer loyalty, Training and Development Journal, 45, 34
  40. Stützle, 2000, MAX-MIN ant system, Future Generation Computer Systems, 16, 889, 10.1016/S0167-739X(00)00043-1 · DOI
  41. Thomassey, 2007, A neural clustering and classification system for sales forecasting of new apparel items, Applied Soft Computing, 7, 1177, 10.1016/j.asoc.2006.01.005 · doi:10.1016/j.asoc.2006.01.005
  42. Vandecruys, 2008, Mining software repositories for comprehensible software fault prediction models, Journal of Systems and Software, 81, 823, 10.1016/j.jss.2007.07.034 · DOI
  43. Van den Poel, 2004, Customer attrition analysis for financial services using proportional hazard models, European Journal of Operational Research, 157, 196, 10.1016/S0377-2217(03)00069-9 · DOI · Paper page
  44. Vanthienen, 1998, A tool-supported approach to inter-tabular verification, Expert Systems with Applications, 15, 277, 10.1016/S0957-4174(98)00047-5 · doi:10.1016/s0957-4174(98)00047-5
  45. Wei, 2002, Turning telecommunications call details to churn prediction: A data mining approach, Expert Systems with Applications, 23, 103, 10.1016/S0957-4174(02)00030-1 · DOI · Paper page

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Metadata

Authors
Verbeke, W., Martens, D., Mues, C., & Baesens, B.
Venue
Expert Systems with Applications
Publisher
Elsevier BV
Published
2011-03
Volume
38
Issue
3
Pages
2354-2364
ISSN
0957-4174 (ISSN-L)
DOI
10.1016/j.eswa.2010.08.023
OpenAlex
W2054640944
Cited by
363
References
7
FWCI
46.34
Type
journal article
Language
en
Open access
closed
Licence
www.elsevier.com/tdm/userlicense/1.0/ (tdm)

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CoChat. (2026). Citation check: Building comprehensible customer churn prediction models with advanced rule induction techniques. https://app.cochat.ai/papers/10.1016/j.eswa.2010.08.023
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Verbeke, W., Martens, D., Mues, C., & Baesens, B. (2011). Building comprehensible customer churn prediction models with advanced rule induction techniques. Expert Systems with Applications. https://doi.org/10.1016/j.eswa.2010.08.023

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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.