A random method for quantifying changing distributions in data streams

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 3721 LNAI, Issue , 2005, Pages 684-691

  Wang, Haixun ^a   Pei, Jian ^b  

^a IBM T J WATSON RESEARCH CENTER   (United States)

^b SIMON FRASER UNIVERSITY   (Canada)

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL INTELLIGENCE; CLASSIFICATION (OF INFORMATION); COMPUTATIONAL COMPLEXITY; COSTS; DATABASE SYSTEMS; LEARNING SYSTEMS; PROBABILITY DISTRIBUTIONS; SECURITY OF DATA;

HIGH DIMENSIONAL DATASETS; INTRUSION DETECTION; PERFORMING CLASSIFICATION; STREAMING DATA ENVIRONMENT;

DATA REDUCTION;

EID: 33646388935     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/11564126_73     Document Type: Conference Paper

References (13)

1. Charu C. Aggarwal. A framework for diagnosing changes in evolving data streams. In SIGMOD, 2003.
2. Eric Bauer and Ron Kohavi. An empirical comparison of voting classification algorithms: Bagging, boosting, and variants. Machine Learning, 36(1-2):105-139, 1999.
3. L. Breiman. Random forests. Machine Learning, 45(1):5-32, 2001.
4. Sergey Brin. Near neighbor search in large metric spaces. In VLDB, Switzerland, 1995.
5. Thomas M. Cover. Elements of Information Theory. Wiley-Interscience, 1991.
6. Yoav Freund and Robert E. Schapire. Experiments with a new boosting algorithm. In ICML, pages 148-156, 1996.
7. L. Hansen and P. Salamon. Neural network ensembles. IEEE Transactions on Pattern Analysis and Machine Intelligence, 12:993-1001, 1990.
8. G. Hulten, L. Spencer, and P. Domingos. Mining time-changing data streams. In SIGKDD, pages 97-106, San Francisco, CA, 2001. ACM Press.
9. A. Krogh and J. Vedelsby. Neural network ensembles, cross validation, and active learning. In Advances in Neural Information Processing Systems, volume 7, pages 231-238. MIT Press, 1995.
10. Junshui Ma and Simon Perkins. Online novelty detection on temporal sequences. In SIGKDD, 2003.
11. M. A. Oliver and R. Webster. Kriging: a method of interpolation for geographical information systems. International Journal Geographic Information Systems, 4(3), 1990.
12. Kagan Turner and Joydeep Ghosh. Error correlation and error reduction in ensemble classifiers. Connection Science, 8(3-4):385-403, 1996.
13. Haixun Wang, Wei Fan, Philip S. Yu, and Jiawei Han. Mining concept-drifting data streams using ensemble classifiers. In SIGKDD, 2003.