Dealing with concept drift and class imbalance in multi-label stream classification

IJCAI International Joint Conference on Artificial Intelligence

Volumn , Issue , 2011, Pages 1583-1588

  Xioufis, Eleftherios Spyromitros ^a   Spiliopoulou, Myra ^b   Tsoumakas, Grigorios ^a   Vlahavas, Ioannis ^a  

^a ARISTOTLE UNIVERSITY OF THESSALONIKI   (Greece)

^b OTTO VON GUERICKE UNIVERSITY   (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

CLASS IMBALANCE PROBLEMS; EMPIRICAL - COMPARISONS; K NEAREST NEIGHBOR ALGORITHM; NEGATIVE EXAMPLES; REAL-WORLD DATASETS; SPACE EFFICIENCIES; STREAM CLASSIFICATION; THRESHOLDING TECHNIQUES;

PATTERN RECOGNITION;

ARTIFICIAL INTELLIGENCE;

EID: 83655189796     PISSN: 10450823     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.5591/978-1-57735-516-8/IJCAI11-266     Document Type: Conference Paper

References (14)

1. P. Domingos and G. Hulten. Mining high-speed data streams. In Proceedings of the 6th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pages 71-80, 2000.
2. M. Gaber, A. Zaslavsky, and S. Krishnaswamy. A survey of classification methods in data streams. In Data Streams: Models and Algorithms, chapter 3, pages 39-59. Springer, 2007.
3. J. Gama, R. Sebastião, and P.P. Rodrigues.Issues in evaluation of stream learning algorithms. In Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pages 329-338, 2009.
4. J. Gao, W. Fan, J. Han, and P.S. Yu. A general framework for mining concept-drifting data streams with skewed distributions. In Proceedings of the 7th SIAM International Conference on Data Mining (SDM'07), pages 3-14, 2007.
5. S. Godbole and S. Sarawagi. Discriminative methods for multi-labeled classification. In Proceedings of the 8th Pacific-Asia Conference on Knowledge Discovery and Data Mining, pages 22-30, 2004.
6. R. Klinkenberg and T. Joachims. Detecting concept drift with support vector machines. In Proceedings of the 17th International Conference on Machine Learning, pages 487-494, 2000.
7. D.D. Lewis, Y. Yang, T.G. Rose, and F. Li. Rcv1: A new benchmark collection for text categorization research. Journal of Machine Learning Research, 5:361-397, December 2004.
8. W. Qu, Y. Zhang, J. Zhu, and Q. Qiu. Mining Multi-label Concept-Drifting Data Streams Using Dynamic Classifier Ensemble. In Proceedings of the 1st Asian Conference on Machine Learning, pages 308-321, 2009.
9. J. Read, B. Pfahringer, G. Holmes, and E. Frank. Classifier chains for multi-label classification. In Proceedings of ECML PKDD '09, pages 254-269, 2009.
10. J. Read, A. Bifet, G. Holmes, and B. Pfahringer. Efficient multi-label classification for evolving data streams. Technical Report, April 2010.
11. A.N. Srivastava and B. Zane-Ulman. Discovering recurring anomalies in text reports regarding complex space systems. In Proceedings of IEEE Aerospace Conference, pages 3853 -3862, 2005.
12. G. Tsoumakas, I. Katakis, and I. Vlahavas. Mining multi-label data. In Data Mining and Knowledge Discovery Handbook, chapter 34, pages 667-685. Springer, 2nd edition, 2010.
13. K. Ueno, X. Xi, E. Keogh, and D.-J. Lee. Anytime classification using the nearest neighbor algorithm with applications to stream mining. IEEE International Conference on Data Mining, pages 623-632, 2006.
14. B. Wenerstrom and C. Giraud-Carrier. Temporal data mining in dynamic feature spaces. In Proceedings of the Sixth International Conference on Data Mining, ICDM '06, pages 1141-1145, Washington, DC, USA, 2006. IEEE Computer Society.