An active learning system for mining time-changing data streams

Intelligent Data Analysis

Volumn 11, Issue 4, 2007, Pages 401-419

  Huang, Shucheng ^a   Dong, Yisheng ^a  

^a SOUTHEAST UNIVERSITY   (China)

Author keywords

Active learning; Concept drift; Increasing iterative step size; Ming time changing data streams; Significant changes; Uncertainty sampling

Indexed keywords

EID: 41649099657     PISSN: 1088467X     EISSN: 15714128     Source Type: Journal    
DOI: 10.3233/ida-2007-11406     Document Type: Article

References (30)

1. C. Aggarwal, J. Han et al., On demand classification of data streams, in Proceedings of Int. Conf. on Knowledge Discovery and Data Mining, 2004.
2. B. Babcock, S. Babu, M. Datar et al., Models and issues in data stream systems, in Proceedings of the ACM Symposium on Principles of Database Systems, ACM Press, June 2002.
3. D. Cohn, Z. Ghahramani and M. Jordan, Active learning with statistical models, Journal of Artificial Intelligence Research 4 (1996), 129-145.
4. D. Cohn, L. Atlas et al., Improving generalization with active learning, Machine Learning 15(2) (1994).
5. P. Cunningham, N. Nowlan, S.J. Delany and M. Haahr, A case-based approach to spam filtering that can track concept drift, in Proceedings of ICCBR Workshop on Long-Lived CBR Systems, 2003.
6. I. Dagan and S. Engelson, Committee-based sampling for training probabilistic classifiers, in International Conference on Machine Learning, 1995.
7. B. David, G. Johannes et al., Detecting change in data streams, in Proceedings of International Conference on Very Large Data Bases, Toronto, Canada, 2004, 180-191.
8. W. Fan, Y. Huang et al., Active mining of data streams, in Proceedings of Fourth SIAM International Conference on Data Mining, Lake Buena Vista, Florida, USA, 2004, 457-461.
9. W. Fan, Y. Huang et al.. Decision tree evolution using limited number of labeled data Items from drifting data streams, in Proceedings of the 4th IEEE International Conference on Data Mining, Brighton, UK, 2004, 379-382.
10. W. Fan, StreamMiner: A classifier ensemble-based engine to mine concept-drifting data streams, in Proceedings of the 30th VLDB Conference, Toronto, Canada, 2004.
11. W. Fan, Systematic data selection to mine concept-drift data streams, in Proceedings of ACM SIGKDD International Conference on knowledge discovery and data mining, 2004.
12. J. Gama, P. Medas et al., Learning with drift detection, in Proceedings of SBIA, 2004.
13. V. Ganti, J. Gehrke et al., Mining data streams under block evolution, SIGKDD Explorations 3(2) (2002).
14. D. Helmbold and P. Long, Tracking drifting concepts by minimizing disagreement, Machine Learning 14(1) (1994), 27-45.
15. T. Hieu, Active Learning Using Pre-clustering, in International Conference on Machine Learning, 2004.
16. G. Hulten, L. Spencer and P. Domingos, Mining time-changing data streams, in Int'l Conf. on Knowledge Discovery and Data Mining, San Francisco, CA, ACM Press, 2001.
17. R. Klinkenberg, Learning drifting concepts: example selection vs. example weighting, Intelligent Data Analysis, Special Issue on Incremental Learning Systems Capable of Dealing with Concept Drift 8(3) (2004).
18. J. Kolter and M. Maloof, Dynamic weighted majority: a new ensemble method for tracking concept drift. The 3rd IEEE Int. Conf. on Data Mining ICDM, IEEE CS Press, 2003, 123-130.
19. M. Kubat, J. Gama and P.E. Utgo, Boosting Classifiers for Drifting Concepts, Intelligent Data Analysis (2006), Accepted for publication.
20. M. Last, Online Classification of Nonstationary Data Streams, Intelligent Data Analysis 6(2) (2002), 129-147.
21. D. Lewis and A. Gale, A sequential algorithm for training text classifiers, in Proceedings of the Seventeenth Annual ACM-SIGR Conference on Research and Development in Information Retrieval, 1994, 3-12.
22. D. Lewis and J. Catlett, Heterogeneous uncertainty sampling for supervised learning, in Proceedings of the Eleventh International Conference on Machine Learning, 1994, 148-156.
23. M. Mihai et al., Using Multiple Windows to Track Concept Drift, Intelligent Data Analysis 8(1) (2004), 29-59.
24. S. Ruping, Incremental learning with support vector machines, in Proceedings of the 2001 IEEE International Conference on Data Mining, 2001.
25. H. Seung, M. Opper et al., Query by committee, in Proceedings of the Fifth ACM Workshop on Computational Learning Theory, 1992, 287-294.
26. W. Street and Y. Kim, A streaming ensemble algorithm (SEA) for large-scale classification, in Proceedings of 7th ACM SIGKDD Int. Conf. on Knowledge Discovery and Data Mining KDD-2001, ACM Press, 2001, 377-382.
27. T.M. Mitchell, Machine Learning, MCGraw-Hill companies, Inc., 1997.
28. S. Tong and D. Koller, Support vector machine active learning with applications to text classification, in Proceedings of the Seventeenth International Conference on Machine Learning, 2000.
29. H. Wang, W. Fan et al, Mining Concept-Drifting data streams using ensemble classifiers, in the 9th ACM International Conference on Knowledge Discovery and Data Mining (SIGKDD), 2003.
30. G. Widmer and M. Kubat, Effective learning in dynamic environments by explicit context tracking, in Proceedings of 6th European Conf. on Machine Learning ECML, Springer-Verlag, Lecture Notes in Computer Science 667, 1993, 227-243.