A concept similarity based data stream classification model

Journal of Information and Computational Science

Volumn 10, Issue 4, 2013, Pages 949-957

  Feng, Lin ^a   Chen, Feng ^a   Yao, Yuan ^a  

^a DALIAN UNIVERSITY OF TECHNOLOGY   (China)

Author keywords

Bootstrap; Classifier pool; Concept drift; Data stream classification; KL divergence

Indexed keywords

BOOTSTRAP; CONCEPT DRIFTS; CONCEPT SIMILARITY; DATA BLOCKS; DATA MINING COMMUNITY; DATA STREAM CLASSIFICATIONS; KL-DIVERGENCE; REAL-WORLD;

DATA COMMUNICATION SYSTEMS; LAKES;

DATA MINING;

EID: 84875790944     PISSN: 15487741     EISSN: None     Source Type: Journal    
DOI: None     Document Type: Article

References (16)

1. H. X. Wang, W. Fan, S. P. Philip, J. W. Han, Mining concept-drifting data streams using ensemble classifiers, in: 9th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD'03), 2003, 226-235
2. G. Widmer, M. Kubat, Learning in the presence of concept drift and hidden contexts, Machine Learning, 23 (1996), 69-101
3. P. Domingos, G. Hulten, Mining high-speed data streams, in: the Sixth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2000, 71-80
4. J. Gama, P. Medas, R. Rocha, Forest trees for on-line data, in: the 2004 ACM Symposium on Applied Computing, 2004, 632-636
5. J. Gama, R. Rocha, P. Medas, Accurate decision trees for mining high-speed data streams, in: Proceedings of the Ninth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2003, 523-528
6. J. Gama, P. Medas, P. Rodrigues, Learning decision trees from dynamic data streams, in: Proceedings of the 2005 ACM Symposium on Applied Computing, 2005, 573-577
7. T. Dasu, S. Krishnan, K. Venkatasubramanian, K. Yi, An information-theoretic approach to detecting changes in multi-dimensional data streams, in: Proc. Symp. on the Interface of Statistics, Computing Science, and Applications, 2006, 1-12
8. H. Borchani, P. Larranaga, C. Bielza, Classifying evolving data streams with partially labeled data, Intelligent Data Analysis, 15(2011), 655-670
9. W. N. Street, Y. S. 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, 2001, 377-382
10. J. Z. Kolter, M. A. Maloof, Dynamic weighted majority: A new ensemble method for tracking concept drift, in: Third IEEE International Conference on Data Mining, 2003, 123-130
11. J. Z. Kolter, M. A. Maloof, Using additive expert ensembles to cope with concept drift, in: Proceedings of the 22nd International Conference on Machine Learning, 2005, 449-459
12. F. Chu, C. Zaniolo, Fast and light boosting for adaptive mining of data streams, Advances in Knowledge Discovery and Data Mining, 18 (2004), 282-292
13. J. C. Schlimmer, R. H. Granger, Incremental learning from noisy data, Machine Learning, 1 (1986), 317-354
14. J. Gao, Classifying data streams with skewed class distributions and concept drifts, Internet Computing, 12 (2008), 37-49
15. G. Hulten, L. Spencer, P. Domingos, Mining time-changing data streams, in: Proceedings of the Seventh ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2001, 97-106
16. L. L. Minku, A. P. White, X. Yao, The impact of diversity on online ensemble learning in the presence of concept drift, IEEE Transactions on Knowledge and Data Engineering, 22 (2010), 730-742