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5. B. Scholkopf and A. Smola, Learning with Kernels: Support Vector Machines, Regu-
larization, Optimization, and Beyond . Cambridge, MA: MIT Press, 2001.
6. C. Burges, “A tutorial on support vector machines for pattern recognition,” Data
Mining and Knowledge Discovery , vol. 2, no. 2, pp. 121-167, 1998.
7. C.-C. Chang and C.-J. Lin, “LIBSVM: A library for support vector machines,” ACM
Transactions on Intelligent Systems and Technology , vol. 2, pp. 1-27, 2011.
8. K. Veropoulos, C. Campbell, and N. Cristianini, “Controlling the sensitivity of support
vector machines,” in Proceedings of the International Joint Conference on Artificial
Intelligence (Stockholm, Sweden), pp. 55-60, 1999.
9. G. Wu and E. Chang, “Adaptive feature-space conformal transformation for
imbalanced-data learning,” in Proceedings of the 20th International Conference on
Machine Learning (Washington, DC), pp. 816-823, IEEE Press, 2003.
10. R. Akbani, S. Kwek, and N. Japkowicz, “Applying support vector machines to
imbalanced datasets,” in Proceedings of the 15th European Conference on Machine
Learning (Pisa, Italy), pp. 39-50, Springer, 2004.
11. N. Chawla, K. Bowyer, L. Hall, and W. Kegelmeyer, “SMOTE: Synthetic minority
over-sampling technique,” Journal of Artificial Intelligence Research , vol. 16, pp.
321-357, 2002.
12. J. Chen, M. Casique, and M. Karakoy, “Classification of lung data by sampling and
support vector machine,” in Proceedings of the 26th Annual International Conference
of the IEEE Engineering in Medicine and Biology Society (San Francisco, CA), vol.
2, pp. 3194-3197, 2004.
13. Y. Fu, S. Ruixiang, Q. Yang, H. Simin, C. Wang, H. Wang, S. Shan, J. Liu, and
W. Gao, “A block-based support vector machine approach to the protein homol-
ogy prediction task in kdd cup 2004,” SIGKDD Exploration Newsletters , vol. 6, pp.
120-124, 2004.
14. S. Lessmann, “Solving imbalanced classification problems with support vector
machines,” in Proceedings of the International Conference on Artificial Intelligence
(Las Vegas, NV, USA), pp. 214-220, 2004.
15. R. Batuwita and V. Palade, “An improved non-comparative classification method for
human microrna gene prediction,” in Proceedings of the International Conference on
Bioinformatics and Bioengineering (Athens, Greece), pp. 1-6, IEEE Press, 2008.
16. R. Batuwita and V. Palade, “microPred: Effective classification of pre-miRNAs for
human miRNA gene prediction,” Bioinformatics , vol. 25, pp. 989-995, 2009.
17. R. Batuwita and V. Palade, “Efficient resampling methods for training support vec-
tor machines with imbalanced datasets,” in Proceedings of the International Joint
Conference on Neural Networks (Barcelona, Spain), pp. 1-8, IEEE Press, 2010.
18. J. Yuan, J. Li, and B. Zhang, “Learning concepts from large scale imbalanced data sets
using support cluster machines,” in Proceedings of the 14th Annual ACM International
Conference on Multimedia (Santa Barbara, CA, USA) , pp. 441-450, ACM, 2006.
19. Z. Lin, Z. Hao, X. Yang, and X. Liu, “Several SVM ensemble methods integrated
with under-sampling for imbalanced data learning,” in Proceedings of the 5th Interna-
tional Conference on Advanced Data Mining and Applications (Beijing, China), pp.
536-544, Springer-Verlag, 2009.
20. P. Kang and S. Cho, “EUS SVMS: Ensemble of under-sampled SVMS for data
imbalance problems,” in Proceedings of the 13th International Conference on Neural
Information Processing (Hong Kong, China), pp. 837-846, Springer-Verlag, 2006.
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