Stream classification with recurring and novel class detection using class-based ensemble

Proceedings - IEEE International Conference on Data Mining, ICDM

Volumn , Issue , 2012, Pages 31-40

  Al Khateeb, Tahseen ^a   Masud, Mohammad M ^b   Khan, Latifur ^a   Aggarwal, Charu ^c   Han, Jiawei ^d   Thuraisingham, Bhavani ^a  

^a UNIVERSITY OF TEXAS AT DALLAS   (United States)

^b UNITED ARAB EMIRATES UNIVERSITY   (United Arab Emirates)

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

^d University of Illinois at Urbana Champaign   (United States)

Author keywords

Novel class; Recurring class; Stream classification

Indexed keywords

CLASS-BASED; COMPUTATIONAL RESOURCES; DATA STREAM; DATA STREAM CLASSIFICATIONS; ENSEMBLE APPROACHES; ENSEMBLE TECHNIQUES; NOVEL CLASS; PREDICTION ERRORS; RECURRING CLASS; STATE-OF-THE-ART TECHNIQUES; STREAM CLASSIFICATION;

DATA COMMUNICATION SYSTEMS;

DATA MINING;

EID: 84874099642     PISSN: 15504786     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICDM.2012.125     Document Type: Conference Paper

References (17)

1. G. Hulten, L. Spencer, and P. Domingos, "Mining time-changing data streams," in Proc. SIGKDD, 2001, pp. 97-106.
2. H. Wang, W. Fan, P. S. Yu, and J. Han, "Mining concept-drifting data streams using ensemble classifiers," in Proc. KDD '03, 2003, pp. 226-235.
3. J. Gao, W. Fan, and J. Han., "On appropriate assumptions to mine data streams." in Proc. ICDM, 2007, pp. 143-152.
4. J. Kolter and M. Maloof., "Using additive expert ensembles to cope with concept drift." in Proc. ICML, Aug 2005, pp. 449-456.
5. C. C. Aggarwal, J. Han, J. Wang, and P. S. Yu, "A framework for on-demand classification of evolving data streams," IEEE TKDE, vol. 18, no. 5, pp. 577-589, 2006.
6. A. Bifet, G. Holmes, B. Pfahringer, R. Kirkby, and R. Gavald, "New ensemble methods for evolving data streams," in Proc. SIGKDD, 2009, pp. 139-148.
7. M. M. Masud, J. Gao, L. Khan, J. Han, and B. M. Thuraisingham, "Classification and novel class detection in concept-drifting data streams under time constraints," IEEE TKDE, vol. 23, no. 1, pp. 859-874, 2011.
8. M. M. Masud, Q. Chen, L. Khan, C. C. Aggarwal, J. Gao, J. Han, and B. M. Thuraisingham, "Addressing concept-evolution in concept-drifting data streams," in Proc. ICDM '10, pp. 929-934.
9. Y. Yang, X. Wu, and X. Zhu, "Combining proactive and reactive predictions for data streams," in Proc. SIGKDD, 2005, pp. 710-715.
10. S. Hashemi, Y. Yang, Z. Mirzamomen, and M. Kangavari, "Adapted one-versus-all decision trees for data stream classification," IEEE TKDE, vol. 21, no. 5, pp. 624-637, 2009.
11. P. Zhang, X. Zhu, and L. Guo, "Mining data streams with labeled and unlabeled training examples," in Proc. ICDM '09., 2009, pp. 627-636.
12. G. Widmer and M. Kubat, "Learning in the presence of concept drift and hidden contexts," Machine Learning, vol. 23, no. 1, pp. 69-101, April 1996.
13. P. Wang, H. Wang, X. Wu, W. Wang, and B. Shi, "A lowgranularity classifier for data streams with concept drifts and biased class distribution," IEEE TKDE, vol. 19, pp. 1202- 1213, 2007.
14. P. Zhang, J. Li, P. Wang, B. J. Gao, X. Zhu, and L. Guo, "Enabling fast prediction for ensemble models on data streams," in Proc. SIGKDD, 2011, pp. 177-185.
15. P. Zhang, X. Zhu, J. Tan, and L. Guo, "Classifier and cluster ensembles for mining concept drifting data streams," in Proc. ICDM '10, pp. 1175-1180.
16. E. J. Spinosa, A. P. de Leon F. de Carvalho, and J. Gama, "Cluster-based novel concept detection in data streams applied to intrusion detection in computer networks," in Proc. ACM SAC, 2008, pp. 976-980.
17. M. M. Masud, T. M. Al-Khateeb, L. Khan, C. C. Aggarwal, J. Gao, J. Han, and B. M. Thuraisingham, "Detecting recurring and novel classes in concept-drifting data streams," in Proc. ICDM '11, Dec. 2011, pp. 1176-1181.