Decision trees for mining data streams

Intelligent Data Analysis

Volumn 10, Issue 1, 2006, Pages 23-45

  Gama, João ^a   Fernandes, Ricardo ^b   Rocha, Ricardo ^b  

^a UNIVERSITY OF PORTO   (Portugal)

^b UNIVERSITY OF AVEIRO   (Portugal)

Author keywords

concept drift; Data streams; incremental decision trees

Indexed keywords

CLASSIFICATION (OF INFORMATION); DECISION TREES;

CLASS DISTRIBUTIONS; CLASSIFICATION TECHNIQUE; CONCEPT DRIFTS; DATA STREAM; DECISION TREE MODELS; DECISION-TREE ALGORITHM; INITIAL PARAMETER; TIME PROPERTIES;

TREES (MATHEMATICS);

EID: 33749419375     PISSN: 1088467X     EISSN: 15714128     Source Type: Journal    
DOI: 10.3233/ida-2006-10103     Document Type: Article

References (34)

1. C. Blake, E. Keogh and C. Merz, UCI repository of Machine Learning databases, 1999.
2. H. Bock and E. Diday, Analysis of symbolic data: Exploratory Methods for Extracting Statistical Information from Complex Data, Springer Verlag, 2000.
3. L. Breiman, Arcing classifiers, The Annals of Statistics 26(3) (1998), 801-849.
4. J. Catlett, Megainduction: a test flight, in Machine Learning: Proceedings of the 8 International Conference, Morgan Kaufmann, 1991.
5. P. Domingos, A unified bias-variance decomposition and its applications, in: Machine Learning, Proceedings of the 17th International Conference, P. Langley, ed., Morgan Kaufmann, 2000.
6. P. Domingos and G. Hulten, Mining high-speed data streams, in: Knowledge Discovery and Data Mining, 2000, pp. 71-80.
7. P. Domingos and M. Pazzani, On the optimality of the simple Bayesian classifier under zero-one loss, Machine Learning 29 (1997), 103-129.
8. R. Duda, P. Hart and D. Stork, Pattern Classification, New York, Willey and Sons, 2001.
9. F. Esposito, D. Malerba and G. Semeraro, A comparative analysis of methods for pruning decision trees, IEEE Transactions on Pattern Analysis and Machine Intelligence 19(5) (1997), 476-491.
10. J. Gama, An analysis of functional trees, in: Machine Learning, Proceedings of the 19th International Conference, C. Sammut, ed., Morgan Kaufmann, 2002.
11. J. Gama, Functional trees, Machine Learning 55(3) (2004), 219-250.
12. J. Gama, R. Rocha and P. Mendas, Accurate decision trees for mining high-speed data streams, in Conference on Knowledge Discovery in Data archive, Proceedings of the ninth ACM IGKDD international conference on Knowledge discovery and data mining, 2003, pp. 523-528.
13. J. Gratch, Sequential inductive learning, in: Proceedings of Thirteenth National Conference on Artificial Intelligence, (Vol. 1), 1996, pp. 779-786.
14. G. Hulten and P. Domingos, Catching up with the data: research issues in mining data streams, in Proc. of Workshop on Research issues in Data Mining and Knowledge Discovery, 2001.
15. G. Hulten and P. Domingos, VFML-A toolkit for mining high-speed time-changing data streams, 2003. http://www.cs.washington.edu/dm/vfml/.
16. G. Hulten, L. Spencer and P. Domingos, Mining time-changing data streams, in Proceedings of the Seventh ACMSIGKDD International Conference on Knowledge Discovery and Data Mining, San Francisco, CA, ACM Press, 2001, 97-106.
17. D. Kalles and T. Morris, Efficient incremental induction of decision trees, Machine Learning 24(3) (1996), 231-242.
18. R. Klinkenberg, Learning drifting concepts: Example selection vs. example weighting, In Intelligent Data Analysis (IDA), Special Issue on Incremental Learning Systems Capable of Dealing with Concept Drift 8(3) (2004).
19. R. Klinkenberg and S. R̈uping, Concept drift and the importance of examples, Text Mining-Theoretical Aspects and Applications (2003), 55-77.
20. R. Kohavi, Scaling up the accuracy of naive Bayes classifiers: a decision tree hybrid, in Proceedings of the 2nd International Conference on Knowledge Discovery and Data Mining, 1996, AAAI Press.
21. R. Kohavi, C. Brodley, B. Frasca, L. Mason and Z. Zheng, KDD-Cup 2000 organizers' report: Peeling the onion, SIGKDD Explorations 2(2) (2000), 86-98. http://www.ecn.purdue.edu/KDDCUP.
22. J. Kolter and M. Maloof, Dynamic weighted majority: A new ensemble method for tracking concept drift, Technical Report, CSTR-20030610-3, 2003.
23. M. Lazarescu, S. Venkatesh and H. Bui, Using multiple windows to track concept drift, Intelligent Data Analysis Journal 8(1) (2004), 29-59.
24. PmatE, Projecto mateḿatica ensino, 2005. http://pmate.ua.pt.
25. R. Quinlan, C4.5: Programs for Machine Learning, Morgan Kaufmann Publishers, Inc., 1993.
26. J. Schlimmer and R. Granger, Beyond incremental processing: Tracking concept drift, in Proceedings of the Fifth National Conference on Artificial Intelligence, AAAI Press, 1986, 502-507.
27. A. Seewald, J. Petrak and G.Widmer, Hybrid decision tree learners with alternative leaf classifiers: an empirical study, in Proceedings of the FLAIRS Conference, AAAI, 2001.
28. W.N. Street and Y. Kim, A streaming ensemble algorithm (sea) for large-scale classification, in Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining, ACM Press, 2001, 377-382.
29. P. Utgoff, ID5: An incremental ID3, in Fifth International Conference on Machine Learning, Morgan Kaufmann Publishers, 1988, 107-120.
30. P. Utgoff, Perceptron trees-A case study in hybrid concept representation, in Proceedings of the Seventh National Conference on Artificial Intelligence, Morgan Kaufmann, 1988.
31. P.E. Utgoff, N.C. Berkman and J.A. Clouse, Decision tree induction based on efficient tree restructuring, Machine Learning 29(1) (1997), 5-44.
32. W. Van de Velde, Incremental induction of topologically minimal trees, in: Machine Learning, Proceedings of the 7th International Conference, B. Porter and R. Mooney, eds, Morgan Kaufmann, 1990.
33. H. Wang, W. Fan, P. Yu and J. Han, Mining concept-drifting data streams using ensemble classifiers, in Proceedings of the ninth ACMSIGKDD international conference on Knowledge discovery and data mining, ACMPress, 2003, 735-740.
34. G. Widmer and M. Kubat, Learning in the presence of concept drift and hidden contexts, Machine Learning 23 (1996), 69-101.