Combining proactive and reactive predictions for data streams

Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

Volumn , Issue , 2005, Pages 710-715

  Yang, Ying ^a   Wu, Xindong ^b   Zhu, Xingquan ^b  

^a MONASH UNIVERSITY   (Australia)

^b University of Vermont   (United States)

Author keywords

Conceptual equivalence; Data stream; Proactive learning

Indexed keywords

BENCHMARKING; LEARNING SYSTEMS;

CONCEPTUAL EQUIVALENCE; DATA STREAMS; PROACTIVE LEARNING;

DATA MINING;

EID: 32344442287     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1081870.1081961     Document Type: Conference Paper

References (15)

1. C. C. Aggarwal, J. Han, J. Wang, and P. S. Yu. A framework for clustering evolving data streams. In Proc. of 29th VLDB, pages 81-92, 2003.
2. V. Ganti, J. Gehrke, and R. Ramakrishnan. Demon: Mining and monitoring evolving data. IEEE TKDE, 13(1):50-63, 2001.
3. G. Hulten, L. Spencer, and P. Domingos. Mining time-changing data streams. In Proc. of 7th SIGKDD, pages 97-106, 2001.
4. E. Keogh and S. Kasetty. On the need for time series data mining benchmarks: a survey and empirical demonstration. In Proc. of 8th SIGKDD, pages 102-111, 2002.
5. J. Z. Kolter and M. A. Maloof. Dynamic weighted majority: A new ensemble method for tracking concept drift. In Proc. of 3rd ICDM, pages 123-130, 2003.
6. C. Lanquillon and I. Renz. Adaptive information filtering: Detecting changes in text streams. In Proc. of 8th CIKM, pages 538-544, 1999.
7. J. R. Quinlan. C4.5: Programs for Machine Learning. Morgan Kaufmann Publishers, 1993.
8. M. Salganicoff. Tolerating concept and sampling shift in lazy learning using prediction error context switching. Artificial Intelligence Review, 11(1-5):133-155, February 1997.
9. K. O. Stanley. Learning concept drift with a committee of decision trees, 2003. Technical Report AI-03-302, Department of Computer Sciences, University of Texas at Austin, USA.
10. W. N. Street and Y. Kim. A streaming ensemble algorithm (sea) for large-scale classification. In Proc. of 7th SIGKDD, pages 377-382, 2001.
11. A. Tsymbal. The problem of concept drift: definitions and related work, 2004. Technical Report TCD-CS-2004-15, Computer Science Department, Trinity College Dublin, Ireland.
12. H. Wang, W. Fan, P. S. Yu, and J. Han. Mining concept-drifting data streams using ensemble classifiers. In Proc. of 9th SIGKDD, pages 226-235, 2003.
13. G. Widmer and M. Kubat. Learning in the presence of concept drift and hidden contexts. Machine Learning, 23(1):69-101, 1996.
14. Y. Yang, X. Wu, and X. Zhu. Dealing with predictive-but-unpredictable attributes in noisy data sources. In Proc. of 8th PKDD, pages 471-483, 2004.
15. Y. Yang, X. Wu, and X. Zhu. Proactive-reactive prediction for data streams, 2005. Technical Report CS-05-03, Department of Computer Sciences, University of Vermont, USA.