Mining concept drifts from data streams based on multi-classifiers

Proceedings - 21st International Conference on Advanced Information Networking and Applications Workshops/Symposia, AINAW'07

Volumn 1, Issue , 2007, Pages 257-263

  Sun, Yue ^a   Mao, Guojun ^a   Liu, Xu ^a   Liu, Chunnian ^a  

^a BEIJING UNIVERSITY OF TECHNOLOGY   (China)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; DATA STRUCTURES; DATA TRANSFER;

DATA STREAMS; MINING CONCEPT DRIFTS;

DATA MINING;

EID: 35248831258     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/AINAW.2007.250     Document Type: Conference Paper

References (19)

1. Widmer G, Kubat M., "Learning in the presence of concept drift and hidden contexts", Machine Learning, 23 (1), 1996, pp69-101.
2. Tsymbal A., "The problem of concept drift: definitions and related work", Technical Report TCD-CS-2004-15, Department of Computer Science, Trinity College Dublin, Apr. 2004.
3. Widmer G, Kubat M. "Effective learning in dynamic environments by explicit context tracking", Proc. 6th European Conf. on Machine Learning, Springer-Verlag, Lecture Notes in Computer Science 667, 1993, pp227-243.
4. Widmer G., "Tracking Context Changes through Meta-Learning", Machine Learning, 27(3), 1997, pp259-286
5. Harries M, Sammut C, Horn K., "Extracting hidden context", Machine Learning, 32(2), 1998, pp101-126.
6. Widyantoro D, Ioerger T, Yen J., "An adaptive algorithm for learning changes in user interests", Proc. of the 8th International Conference on Information and Knowledge Management, Kansas City, Nov. 1999, pp405-412.
7. Rushing J, Graves S, Criswell E, Lin A., "A coverage based ensemble algorithm (CBEA) for streaming data", Proc. of the 16th IEEE International Conference on Tools with Artificial Intelligence (ICTAI'04), IEEE Computer Society, Washington, Nov. 2004, pp106-112
8. Street W, Kim Y, "A streaming ensemble algorithm (SEA) for large-scale classification", Proc. of the 7th ACM International Conference on Knowledge Discovery and Data Mining, ACM Press, New York, NY, 2001, pp377-382,
9. Schlimmer J.C., Granger R.H., "Incremental learning from noisy data", Machine Learning, 1(3), 1986, pp317-354.
10. Widmer G., Kubat M., "Learning in the presence of concept drift and hidden contexts", Machine Learning, 23 (1), 1996, pp69-101.
11. Kuh A., Petsche T., Rivest R.L., "Learning time-varying concepts", Advances in Neural Information Processing Systems (NIPS), Morgan Kaufmann, 1991, pp183-189.
12. Domingos P, Hulten G., "Mining high-speed data streams", Proc. of the Association for Computing Machinery 6th International Conference on Knowledge Discovery and Data Mining, ACM Press, New York, NY, 2000, pp71-80.
13. Hulten G, Spencer L, Domingos P., "Mining time-changing data streams", Proc. of the 7th ACM SIGKDD Int. Conf. on Knowledge Discovery and Data Mining - KDD'01, ACM Press, New York, NY, 2001, pp97-106.
14. Gama J, Rocha R, Medas P., "Accurate decision trees for mining high-speed data streams", Proc. of the 9th International Conference on Knowledge Discovery and Data Mining, ACM Press, New York, NY, 2003, pp523-528.
15. Gama J, Medas P, Rocha R., "Forest trees for on-line data", ACM Symposium on Applied Computing - SAC04, ACM Press, New York, NY, 2004, pp632-636.
16. Gama J, Medas P, Rodrigues P., "Learning decision trees from dynamic data streams", ACM Symposium on Applied Computing - SAC05, ACM Press, New York, NY, 2005, pp573-577.
17. Wang H, Fan W, Yu P, Han J., "Mining concept-drifting data streams using ensemble classifiers", Proc. of the 9th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, ACM Press, New York, NY, 2003, pp226-235
18. Chu F, Zaniolo C., "Fast and light boosting for adaptive mining of data streams", Proc. of the 5th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD), Sydney, May 2004, pp282-292.
19. Ian H. Witten and Eibe Frank, Data Mining: Practical machine learning tools and techniques, 2nd Edition, Morgan Kaufmann, San Francisco, 2005.