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22. J. Zhu and E. Hovy, “Active learning for word sense disambiguation with methods
for addressing the class imbalance problem,” in
EMNLP-CoNLL 2007
(Prague, Czech
Republic), 2007.
23. G. Schohn and D. Cohn, “Less is more: Active learning with support vector
machines,” in
Proceedings of the 17th International Conference on Machine Learning
(ICML)
(Stanford, CA, USA), pp. 839-846, Morgan Kaufmann, 2000.
24. A. Bordes, S. Ertekin, J. Weston, and L. Bottou, “Fast kernel classifiers with online
and active learning,”
Journal of Machine Learning Research
, vol. 6, pp. 1579-1619,
2005.
25. J. Huang, S. Ertekin, and C. L. Giles, “Efficient name disambiguation for large
scale datasets,” in
Proceedings of European Conference on Principles and Prac-
tice of Knowledge Discovery in Databases (ECML/PKDD)
(Berlin, Germany), vol.
4213/2006, pp. 536-544, 2006.
26. V. Vapnik,
The Nature of Statistical Learning Theory
. New York: Springer, 1995.
27. A. J. Smola and B. Scholkopf, “Sparse greedy matrix approximation for machine
learning,” in
Proceedings of 17th International Conference on Machine Learning
(ICML)
(Stanford, CA), pp. 911-918, Morgan Kaufmann, 2000.
28. M. Kubat and S. Matwin, “Addressing the curse of imbalanced training datasets:
One sided selection,” in
Proceedings of 14th International Conference on Machine
Learning (ICML)
, vol. 30, no. 2-3, pp. 195-215, 1997.
29. N. Japkowicz and S. Stephen, “The class imbalance problem: A systematic study,”
Intelligent Data Analysis
, vol. 6, no. 5, pp. 429-449, 2002.
30. N. V. Chawla, K. W. Bowyer., L. O. Hall, and W. P. Kegelmeyer, “SMOTE: Synthetic
minority over-sampling technique,”
Journal of Artificial Intelligence Research
, vol.
16, pp. 321-357, 2002.
31. N. Japkowicz, “The class imbalance problem: Significance and strategies,” in
Pro-
ceedings of 2000 International Conference on Artificial Intelligence (IC-AI'2000)
, vol.
1, pp. 111-117, 2000.
32. C. X. Ling and C. Li, “Data mining for direct marketing: Problems and solutions,”
in
Knowledge Discovery and Data Mining
, pp. 73-79, 1998.
33. J. Attenberg and F. Provost, “Why label when you can search? Strategies for applying
human resources to build classification models under extreme class imbalance,” in
KDD
, pp. 423-432, ACM, 2010.
34. C. Perlich and G. Swirszcz, “On cross-validation and stacking: Building seemingly
predictive models on random data,”
SIGKDD Explorations
, vol. 12, no. 2, p. 11-15,
2010.
35. J. He and J. G. Carbonell, “Nearest-neighbor-based active learning for rare category
detection,” in
NIPS
(Vancouver, Canada), pp. 633-640, MIT Press, 2007.
36. Z. Xu, K. Yu, V. Tresp, X. Xu, and J. Wang, “Representative sampling for text
classification using support vector machines,” in
ECIR
, pp. 393-407, Springer-Verlag,
2003.
37. Weiss, G. M., “The impact of small disjuncts on classifier learning,”
Annals of Infor-
mation Systems
, vol. 8, pp. 193-226, 2010.
38. R. Lomasky, C. Brodley, M. Aernecke, D. Walt, and M. Friedl, “Active class selec-
tion,”
Machine Learning: ECML
, vol. 4701, pp. 640-647, 2007.
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