Context of the concept drift in data mining: an empirical study on the regional economic influence to the relation between demographic attributes and credit card holder's loyalty

2008 International Conference on Management Science and Engineering 15th Annual Conference Proceedings, ICMSE

Volumn , Issue , 2008, Pages 38-43

  Mo, Jia Hui ^a   Zou, Peng ^a   Chen, Jin ^b  

^a HARBIN INSTITUTE OF TECHNOLOGY   (China)

^b RENMIN UNIVERSITY OF CHINA   (China)

Author keywords

Concept drift; Context factor; Customer loyalty; Data mining

Indexed keywords

INFORMATION MANAGEMENT; MANAGEMENT SCIENCE; REGIONAL PLANNING;

CONCEPT DRIFT; CONTEXT FACTOR; CREDIT CARDS; CUSTOMER LOYALTY; ECONOMIC ENVIRONMENTS; EMPIRICAL STUDIES; INDIVIDUAL CHARACTERISTICS;

DATA MINING;

EID: 57649227490     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICMSE.2008.4668891     Document Type: Conference Paper

References (21)

1. G. Widmer. Learning in dynamically changing domains: Recent contributions of machine learning[C]// Perner Ed. Proceedings of the MLNet Workshop On Learning In Dynamically Changing Domains: Theory Revision And Context Dependence Issues, Prague, Czech Republic, 1997:286-292.
2. M.G. Kelly, D. J. Hand, N. M. Adams. The impact of changing populations on classifier performance[C]// Volkmar ed. Conference on Knowledge Discovery in Data, Proceedings of the Fifth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining., San Diego, California, United States, 1999:332-339.
3. J.C. Schlimmer, R.H.Granger. Incremental learning from noisy data[J]. Machine Learning, 1986, 13(3):297-309.
4. K. B. Pratt, G. Tschapek. Visualizing concept drift[C]//Petra ed. Conference on Knowledge Discovery in Data, Proceedings of the Ninth ACM SIGKDD International Conference on Kowledge Discovery and Data Mining, 2003:412-419.
5. G. Widmer, M. Kubat. Learning flexible concepts from streams of examples: FLORA2[C]// Fink ed. Proceedings of the European Conference on Artificial Intelligence. 1992:189-195.
6. R. Klinkenberg, S. Ruping. Concept drift and the importance of examples[C]// Wysotzki F. ed. Text Mining - Theoretical Aspects and Applications, PhysicaVerlag, Heidelberg, Germany, 2003:78-84.
7. R. Klinkenberg. Learning drifting concepts with partial user feedback[C]// Beiträge zum Treffen der ed. GIFachgruppe Maschinelles Lernen, 1999:31-36.
8. R. Klinkenberg, I. Renz. Adaptive information filtering: learning drifting concepts[C]// Geibel P. ed Germany, Fachbereich Informatik, TU Berlin, 1998.
9. R. Klinkenberg. Meta-Learning, model selection, and example selection in machine learning domains with concept drift[C]// Schadler K. ed. Annual workshop of the special interest group on machine learning, knowledge discovery, and data mining of the German Computer Science Society (Gl), Saarbrucken, Germany, 2005, 79-84.
10. H.X. Wang, W. Fan, P. S. Yu, J.W. Han, Mining concept-drifting data streams using ensemble classifiers[C]// Furnkranz ed. Proceedings of the Ninth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Washington, D.C, 2003: 45-50.
11. H.Wang, W. Fan, P.S.Yu, J. Han. Mining concept-drifting data streams using ensemble classifiers[C]// Johannes and Grieser ed. Proc. 9th ACM SIGKDD Int. Conf. on Knowledge Discovery and Data Mining, ACM Press, 2003:1002-1007.
12. K.O. Stanley. Learning Concept drift with a committee of decision trees [R]. Technical Report AI-03-302, Department of Computer Sciences, University of Texas at Austin, Austin, TX, USA, 2003.
13. M. Black, R. Hickey. Learning classification rules for telecom customer call data under concept drift[J]. Soft Computing, 2003, 8(2):102-108
14. A. Tsymbal. The problem of concept drift: Definitions and related work [R]. Technical Report TCD-CS-2004-15, Computer Science Department, Trinity College Dublin. 2004.
15. R.S. Michalski. How to learn imprecise concepts: A method employing a two-tiered knowledge representation for learning[C]// Proceedings of the Fourth International Workshop on Machine Learning, 1987.
16. G. Widmer, M. Kubat. Learning in the presence of concept drift and hidden contexts [J]. Machine Learning, 1996, 23(1):69-101.
17. Michael Harries, Claude Sammut. Extracting hidden context[J]. Machine Learning, 1998, 25(2):101-126.
18. P.D. Turney. The identication of context-sensitive features: A formal denition of context for concept learning[C]// Trinity ed. 13th International Conference on Machine Learning, Bari, Italy. 1996:43-49.
19. Turney. The management of context-sensitive features: a review of strategies[C]// Gunter ed. 13th International Conference on Machine Learning, Bari, Italy. 1996:56-61.
20. Q. Yang, X.D Wu. 10 Challenging problems in data mining research[J]. International Journal of Information Technology & Decision Making. 2006, 2(6):597-604
21. R. L. Oliver. Whence consumer loyalty?[J]. Journal of Marketing, 1999, 63(1):34-44.