Active query driven by uncertainty and diversity for incremental multi-label learning

Proceedings - IEEE International Conference on Data Mining, ICDM

Volumn , Issue , 2013, Pages 1079-1084

  Huang, Sheng Jun ^a   Zhou, Zhi Hua ^a  

^a NANJING UNIVERSITY   (China)

Author keywords

active learning; diversity; multi label learning; uncertainty

Indexed keywords

EID: 84894644448     PISSN: 15504786     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICDM.2013.74     Document Type: Conference Paper

References (33)

1. M. R. Boutell, J. Luo, X. Shen, and C. M. Brown. Learning multi-label scene classification. Pattern Recognition, 37(9):1757-1771, 2004.
2. K. Brinker. Incorporating diversity in active learning with support vector machines. In ICML, pages 59-66, 2003.
3. K. Brinker. On active learning in multi-label classification. In M. Spiliopoulou, R. Kruse, C. Borgelt, A. Nrnberger, and W. Gaul, editors, From Data and Information Analysis to Knowledge Engineering, pages 206-213. Springer Berlin Heidelberg, 2006.
4. S. Chakraborty, V. Balasubramanian, and S. Panchanathan. Optimal batch selection for active learning in multi-label classification. In Proceedings of the 19th ACM International Conference on Multimedia, pages 1413-1416, 2011.
5. S. Diplaris, G. Tsoumakas, P. A. Mitkas, and I. Vlahavas. Protein classification with multiple algorithms. In Proceedings of the 10th Panhelllenic Conference on Informatics, pages 448-456, 2005.
6. P. Donmez, J. G. Carbonell, and P. N. Bennett. Dual strategy active learning. In ECML, pages 116-127, 2007.
7. P. Duygulu, K. Barnard, J. F. de Freitas, and D. A. Forsyth. Object recognition as machine translation: Learning a lexicon for a fixed image vocabulary. In ECCV, pages 97-112, 2002.
8. A. Elisseeff and J. Weston. A kernel method for multi-labelled classification. In NIPS 14, pages 681-687. 2001.
9. A. Esuli and F. Sebastiani. Active learning strategies for multi-label text classification. In ECIR, pages 102-113. 2009.
10. R.-E. Fan, K.-W. Chang, C.-J. Hsieh, X.-R. Wang, and C.-J. Lin. Liblinear: A library for large linear classification. JMLR, 9:1871-1874, 2008.
11. J. Fürnkranz, E. Hüllermeier, E. Loza Mencía, and K. Brinker. Multilabel classification via calibrated label ranking. Machine Learning, 73(2):133-153, 2008.
12. S.-J. Huang, W. Gao, and Z.-H. Zhou. Fast multi-instance multi-label learning. In CORR abs/1310.2049, 2013.
13. S.-J. Huang, R. Jin, and Z.-H. Zhou. Active learning by querying informative and representative examples. In NIPS 23, pages 892-900. 2010.
14. C.-W. Hung and H.-T. Lin. Multi-label active learning with auxiliary learner. In ACML, pages 315-330, 2011.
15. B. Klimt and Y. Yang. Introducing the enron corpus. In Proceedinds of the 1st Conference on Email and Anti-Spam, 2004.
16. X. Li and Y. Guo. Active learning with multi-label svm classification. In IJCAI, 2013.
17. X. Li, L. Wang, and E. Sung. Multilabel svm active learning for image classification. In ICIP, pages 2207-2210, 2004.
18. H. T. Nguyen and A. W. M. Smeulders. Active learning using pre-clustering. In ICML, pages 623-630, 2004.
19. J. P. Pestian, C. Brew, P. Matykiewicz, D. J. Hovermale, N. Johnson, K. B. Cohen, and W. Duch. A shared task involving multi-label classification of clinical free text. In Proceedings of the Workshop on Bio NLP 2007: Biological, Translational, and Clinical Language Processing, pages 97-104, 2007.
20. G.-J. Qi, X.-S. Hua, Y. Rui, J. Tang, and H.-J. Zhang. Twodimensional active learning for image classification. In CVPR, pages 1-8, 2008.
21. H. Robbins and S. Monro. A stochastic approximation method. The Annals of Mathematical Statistics, 22(3):400-407, 1951.
22. F. Sebastiani. Machine learning in automated text categorization. ACM Computing Surveys, 34(1):1-47, 2002.
23. B. Settles. Active learning literature survey. Computer Sciences Technical Report 1648, University of Wisconsin-Madison, 2009.
24. M. Singh, A. Brew, D. Greene, and P. Cunningham. Score normalization and aggregation for active learning in multilabel classification. Technical report, University College Dublin, 2010.
25. M. Singh, E. Curran, and P. Cunningham. Active learning for multi-label image annotation. In Proceedings of the 19th Irish Conference on Artificial Intelligence and Cognitive Science, 2008.
26. S. Tong and D. Koller. Support vector machine active learning with applications to text classification. In ICML, pages 999-1006, 2000.
27. K. Trohidis, G. Tsoumakas, G. Kalliris, and I. Vlahavas. Multi-label classification of music into emotions. In Proceedings of the 9th International Conference of Music Information Retrieval, page 325, 2008.
28. N. Usunier, D. Buffoni, and P. Gallinari. Ranking with ordered weighted pairwise classification. In ICML, pages 1057-1064, 2009.
29. J. Weston, S. Bengio, and N. Usunier. Wsabie: Scaling up to large vocabulary image annotation. In IJCAI, pages 2764-2770, 2011.
30. B. Yang, J.-T. Sun, T. Wang, and Z. Chen. Effective multilabel active learning for text classification. In KDD, pages 917-926, 2009.
31. M.-L. Zhang and Z.-H. Zhou. ML-kNN: A lazy learning approach to multi-label learning. Pattern Recognition, 40(7):2038-2048, 2007.
32. M.-L. Zhang and Z.-H. Zhou. A review on multi-label learning algorithms. TKDE, in press.
33. X. Zhang, J. Cheng, C. Xu, H. Lu, and S. Ma. Multi-view multi-label active learning for image classification. In Proceedings of the IEEE International Conference on Multimedia and Expo, pages 258-261, 2009.