Entropy-based concept shift detection

Proceedings - IEEE International Conference on Data Mining, ICDM

Volumn , Issue , 2006, Pages 1113-1118

  Vorburger, Peter ^a   Bernstein, Abraham ^a  

^a UNIVERSITY OF ZURICH   (Switzerland)

Author keywords

[No Author keywords available]

Indexed keywords

DATA STREAMS; SYNTHETIC DATA SETS; WEARABLE DEVICES;

ALGORITHMS; COMPUTATIONAL EFFICIENCY; DATA REDUCTION; DATA STRUCTURES; DATABASE SYSTEMS;

SENSORY PERCEPTION;

EID: 34748829116     PISSN: 15504786     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICDM.2006.66     Document Type: Conference Paper

References (8)

1. A. Bernstein and P. Vorburger. A scenario-based approach for direct interruptablity prediction on wearable devices. Journal of Pervasive Computing and Communications, 2006.
2. D. P. Helmbold and P. M. Long. Tracking drifting concepts by minimizing disagreements. Mach. Learn, 14:27-45, 1994.
3. J. Z. Kolter and M. A. Maloof. Dynamic weighted majority: A new ensemble method for tracking concept drift. In ICDM, pages 123-130, 2003.
4. A. Kuh, T. Petsche, and R. L. Rivest. Learning time-varying concepts. In NIPS-3: Proceedings of the 1990 conference on Advances in neural information processing systems 3, pages 183-189. Morgan Kaufmann Publishers Inc., 1990.
5. A. Tsymbal. The problem of concept drift: definitions and related work. 2004.
6. P. Vorburger and A. Bernstein. Entropy-based detection of real and virtual concept shifts. Working Paper - University of Zurich, Department of Informatics, 2006.
7. H. Wang, W. Fan, P. S. Yu, and J. Han. Mining conceptdrifting data streams using ensemble classifiers. In KDD '03: Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining, pages 226-235. ACM Press, 2003.
8. G. Widmer and M. Kubat. Effective learning in dynamic environments by explicit context tracking. In Machine Learning: ECML-93 - Proc. of the European Conference on Machine Learning, pages 227-243. Springer, 1993.