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50. N. Abe, Invited talk: “Sampling approaches to learning from imbalanced datasets:
Active learning, cost sensitive learning and beyond,” in Proceedings of International
Conference on Machine Learning, Workshop on Learning from Imbalanced Data Sets
II , 2003.
51. S. Ertekin, J. Huang, L. Bottou, and L. Giles, “Learning on the border: Active learning
in imbalanced data classification,” in Proceedings of ACM Conference on Information
and Knowledge Management , pp. 127-136, ACM, (Lisboa, Portugal), 2007.
52. S. Ertekin, J. Huang, and C. L. Giles, “Active learning for class imbalance problem,”
in Proceedings of International SIGIR Conference on Research and Development in
Information Retrieval , pp. 823-824, 2007.
53. F. Provost, “Machine learning from imbalanced datasets 101,” Learning from Imbal-
anced Data Sets: Papers from the American Association for Artificial Intelligence
Workshop, Technical Report WS-00-05, 2000.
54. J. Zhu and E. Hovy, “Active learning for word sense disambiguation with meth-
ods for addressing the class imbalance problem,” in Proceedings of Joint Conference
on Empirical Methods in Natural Language Processing and Computational Natural
Language Learning (Prague, Czech Republic), pp. 783-790, Association for Com-
putational Linguistics, 2007.
55. J. Doucette and M. I. Heywood, “GP classification under imbalanced data sets: Active
sub-sampling and AUC approximation,” Lecture Notes in Computer Science , vol.
4971, pp. 266-277, 2008.
56. B. Scholkopt, J. C. Platt, J. Shawe-Taylor, A. J. Smola, and R. C. Williamson, “Esti-
mating the support of a high-dimensional distribution,” Neural Computation , vol. 13,
pp. 1443-1471, 2001.
57. L. M. Manevitz and M. Yousef, “One-class SVMs for document classification,” Jour-
nal of Machine Learning Research , vol. 2, pp. 139-154, 2001.
58. N. Japkowicz, “Supervised versus unsupervised binary-learning by feedforward neural
networks,” Machine Learning , vol. 42, pp. 97-122, 2001.
59. N. Japkowicz, “Learning from imbalanced data sets: A comparison of various strate-
gies,” Proceedings of Learning from Imbalanced Data Sets, the AAAI Workshop,
Technical Report WS-00-05, pp. 10-15, 2000.
60. N. Japkowicz, C. Myers, and M. Gluck, “A novelty detection approach to classifi-
cation,” in Proceedings of Joint Conference on Artificial Intelligence , pp. 518-523,
1995.
61. H. J. Lee and S. Cho, “The novelty detection approach for difference degrees of class
imbalance,” Lecture Notes in Computer Science , vol. 4233, pp. 21-30, 2006.
62. Y. Sun, M. S. Kamel, and Y. Wang, “Boosting for learning multiple classes with
imbalanced class distribution,” in Proceedings of International Conference on Data
Mining , pp. 592-602, IEEE Computer Society, (Washington, DC, USA), 2006.
63. N. Abe, B. Zadrozny, and J. Langford, “An iterative method for multi-class cost-
sensitive learning,” in Proceedings of the Tenth ACM SIGKDD International Con-
ference on Knowledge Discovery and Data Mining (Seattle, WA, USA), pp. 3-11,
ACM, 2004.
64. K. Chen, B. L. Lu, and J. Kwok, “Efficient classification of multi-label and imbal-
anced data using min-max modular classifiers,” in Proceedings of World Congress
on Computation Intelligence - International Joint Conference on Neural Networks ,
pp. 1770-1775, 2006.
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