Information Technology Reference
In-Depth Information
21. Guha, S., Koudas, N., Shim, K.: Data-streams and histograms. In: Proceedings of the 2001
Annual ACM Symposium on Theory of Computing (STOC 2001), Heraklion, Crete, Greece,
pp. 471-475 (2001)
22. Hulten, G., Spencer, L., Domingos, P.: Mining time changing data streams. In: Proceedings
of the 7th International Conference on Knowledge Discovery and Data Mining (KDD 2001),
San Francisco, CA, pp. 97-106 (2001)
23. Klinkenberg, R.: Learning drifting concepts: Example selection vs. example weighting. In-
telligent Data Analysis 8, 281-300 (2004)
24. Kolter, J.Z., Maloof, M.A.: Using additive expert ensembles to cope with concept drift. In:
Proceedings of the 22nd International Conference on Machine learning (ICML 2005), Bonn,
Germany, pp. 449-456 (2005)
25. Kolter, J.Z., Maloof, M.A.: Dynamic weighted majority: An ensemble method for drifting
concepts. Journal of Machine Learning Research 8, 2755-2790 (2007)
26. Oza, N.C., Russell, S.: Online bagging and boosting. In: Proceedings of 8th International
Workshop on Artificial Intelligence and Statistics (AISTATS 2001), Key West, FL, pp. 105-
112 (2001)
27. Pfahringer, B., Holmes, G., Kirkby, R.: Handling Numeric Attributes in Hoeffding Trees.
In: Washio, T., Suzuki, E., Ting, K.M., Inokuchi, A. (eds.) PAKDD 2008. LNCS (LNAI),
vol. 5012, pp. 296-307. Springer, Heidelberg (2008)
28. Schlimmer, J.C., Granger, R.H.: Beyond incremental processing: Tracking concept drift. In:
Proceedings of the 5th National Conference on Artificial Intelligence, Menlo Park, CA, pp.
502-507 (1986)
29. Scholz, M., Klinkenberg, R.: An ensemble classifier for drifting concepts. In: Proceeding of
2nd International Workshop on Knowledge Discovery from Data Streams, in Conjunction
with ECML-PKDD 2005, Porto, Portugal, pp. 53-64 (2005)
30. Street, W.N., Kim, Y.: A streaming ensemble algorithm (SEA) for large-scale classification.
In: Proceedings of the 7th International Conference on Knowledge Discovery and Data Min-
ing (KDD 2001), San Francisco, CA, pp. 377-382 (2001)
31. The UCI KDD: University of California: KDD Cup 1999 Data,
32. The University of Waikato: MOA: Massive Online Analysis (August 2009),
33. The
University
of
Waikato:
Weka
3:
Data
Mining
Software
in
Java,
Version
3.6,
34. Wang, H., Fan, W., Yu, P.S., Han, J.: Mining concept-drifting data streams using ensemble
classifiers. In: Proceedings of the 9th International Conference on Knowledge Discovery and
Data Mining (KDD 2003), Washington, DC, pp. 226-235 (2003)
