An adaptive distributed ensemble approach to mine concept-drifting data streams

Proceedings - International Conference on Tools with Artificial Intelligence, ICTAI

Volumn 2, Issue , 2007, Pages 183-187

  Folino, Gianluigi ^a   Pizzuti, Clara ^a   Spezzano, Giandomenico ^a  

^a ICAR CNR   (Italy)

Author keywords

[No Author keywords available]

Indexed keywords

ADAPTIVE ALGORITHMS; ARTIFICIAL INTELLIGENCE; CLASSIFICATION (OF INFORMATION); CLASSIFIERS; COMPUTER PROGRAMMING; CYBERNETICS; DATA MINING; DECISION MAKING; DECISION THEORY; DECISION TREES; FRACTAL DIMENSION; GENETIC ALGORITHMS; GENETIC PROGRAMMING; LEARNING SYSTEMS;

ADAPTIVE BOOSTING; CAPTURE TIME; CHANGE-DETECTION; CO-EVOLUTIONARY; CONTINUOUS FLOWS; COOPERATIVE MODELS; DATA STREAMING; DISTRIBUTED DATA STREAMS; ENSEMBLE APPROACH; ENSEMBLE BEHAVIOR; ENSEMBLE OF CLASSIFIERS; GENETIC PROGRAMMING (GP); INTERNATIONAL CONFERENCES; REAL-LIFE DATA; SELF SIMILARITIES; TRAINING SETS;

RIVERS;

EID: 48649110463     PISSN: 10823409     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICTAI.2007.51     Document Type: Conference Paper

References (19)

1. The third international knowledge discovery and data mining tools competition dataset kdd99-cup. In http://kdd.ics.uci.edu/databases/kddcup99/ kddcup99.html, 1999.
2. C. C. Aggarwal. Data Streams : Models and Algorithms. Springer, 2007.
3. F. Chu and C. Zaniolo. Fast and light boosting for adaptive mining of data streams. In H. Dai, R. Srikant, and C. Zhang, editors, Proceedings of the 8th Pacific-Asia Conference (PAKDD 2004), May 26-28, 2004, Proceedings, volume 3056 of LNAI, pages 282-292, Sydney, Australia, 2004. Springer Verlag.
4. W. Fan. Systematic data selection to mine concept-drifting data streams. In Proceedings of the 10th ACM SIGKDD Int. Conf. on Knowledge discovery and data mining (KDD'04),, pages 128-137, Seattle, WA, USA, 2004. ACM.
5. G. Folino, C. Pizzuti, and G. Spezzano. Ensembles for large scale data classification. IEEE Transaction on Evolutionary Computation, 10(5):604-616, October 2006.
6. J. Gehrke, V. Ganti, R. Ramakrishnan, and W. Loh. Boat - optimistic decision tree construction. In Proceedings of the ACMSIGMOD International Conference on Management of Data (SIGMOD'99), pages 169-180. ACM Press, 1999.
7. P. Grassberger. Generalized dimensions of strange attractors. Physics Letters, 97A:227-230, 1983.
8. H. Kargupta and B.-H. Park. A fourier spectrum-based approach to represent decision trees for mining data streams in mobile environments. IEEE Transaction on Knowledge and Data Engineering, 16(2):216-229, 2004.
9. J. Z. Kolter and M. A. Maloof. Using additive expert ensembles to cope with concept drift. In Proceedings of the 22nd Int. Conference on Machine Learning (ICML'05), pages 449-456, 2005.
10. J. R. Koza. Genetic Programming: On the Programming of Computers by means of Natural Selection. MIT Press, Cambridge, MA, 1992.
11. L. Liebovitch and T. Toth. A fast algorithm to determine fractal dimensions by box counting. Physics Letters, 141A(8):-, 1989.
12. B. Mandelbrot. The Fractal Geometry of Nature. W.H Freeman, New York, 1983.
13. S. Parthasarathy, A. Ghoting, and M. E. Otey. A survey of distributed mining of data streams. In C. C. Aggarwal, editor, in Data Streams : Models and Algorithms, pages 289-307. Springer, 2007.
14. J. Sarraille and P. DiFalco. FD3. http://tori.postech.ac.kr/ softwares.
15. M. Scholz and R. Klinkenberg. Boosting classifiers for drifting concepts. Intelligent Data Analysis, 11(1):3-28, 2007.
16. W. N. Street and Y. Kim. A streaming ensemble algorithm (sea) for large-scale classification. In Proceedings of the seventh ACM SIGKDD International conference on Knowledge discovery and data mining (KDD'01),, pages 377-382, San Francisco, CA, USA, August 26-29, 2001 2001. ACM.
17. P. E. Utgoff. Incremental induction of decision trees. Machine Learning, 4:161-186, 1989.
18. H. Wang, W. Fan, P. Yu, and J. Han. Mining concept-drifting data streams using ensemble classifiers. In Proceedings of the nineth ACM SIGKDD International conference on Knowledge discovery and data mining (KDD'03), pages 226-235, Washington, DC, USA, August 24-27, 2003 2003. ACM.
19. G. Widmer and M. Kubat. Learning in presence of concept drift and hidden contexts. Machine Learning, (23):69-101, 1996.