CLASS IMBALANCE LEARNING METHODS FOR SUPPORT VECTOR MACHINES - Imbalanced Learning: Foundations, Algorithms, and Applications - page 96

21. Y. Liu, A. An, and X. Huang, “Boosting prediction accuracy on imbalanced datasets
with svm ensembles,” in Proceedings of the 10th Pacific-Asia conference on Advances
in Knowledge Discovery and Data Mining (Singapore), pp. 107-118, Springer Verlag,
2006.
22. B. Wang and N. Japkowicz, “Boosting support vector machines for imbalanced data
sets,” Knowledge and Information Systems , vol. 25, pp. 1-20, 2010.
23. W. Fan, S. Stolfo, J. Zhang, and P. Chan, “Adacost: Misclassification cost-sensitive
boosting,” in Proceedings of the 16th International Conference on Machine Learning
(Bled, Slovenia), pp. 97-105, Morgan Kaufmann Publishers, Inc., 1999.
24. M. Joshi, V. Kumar, and C. Agarwal, “Evaluating boosting algorithms to classify rare
classes: Comparison and improvements,” in Proceedings of the IEEE International
Conference on Data Mining , pp. 257-264, IEEE Computer Society, 2001.
25. N. Chawla, A. Lazarevic, L. Hall, and K. Bowyer, “Smoteboost: Improving prediction
of the minority class in boosting,” in Proceedings of the Principles of Knowledge
Discovery in Databases (Cavtat-Dubrovnik, Croatia), pp. 107-119, Springer Verlag,
2003.
26. B. Raskutti and A. Kowalczyk, “Extreme re-balancing for SVMS: A case study,”
SIGKDD Exploration Newsletters , vol. 6, pp. 60-69, June 2004.
27. A. Kowalczyk and B. Raskutti, “One class SVM for yeast regulation prediction,”
SIGKDD Exploration Newsletters , vol. 4, no. 2, pp. 99-100, 2002.
28. T. Imam, K. Ting, and J. Kamruzzaman, “z-SVM: An SVM for improved classifi-
cation of imbalanced data,” in Proceedings of the 19th Australian Joint Conference
on Artificial Intelligence: Advances in Artificial Intelligence (Hobart, Australia), pp.
264-273, Springer-Verlag, 2006.
29. G. Wu and E. Chang, “Class-boundary alignment for imbalanced dataset learning,”
in Proceeding of the International Conference on Machine Learning: Workshop on
Learning from Imbalanced Data Sets , pp. 49-56, 2003.
30. G. Wu and E. Chang, “KBA: Kernel boundary alignment considering imbalanced
data distribution,” IEEE Transactions on Knowledge and Data Engineering , vol. 17,
no. 6, pp. 786-795, 2005.
31. N. Cristianini, J. Kandola, A. Elisseeff, and J. Shawe-Taylor, “On kernel-target align-
ment,” in Advances in Neural Information Processing Systems 14 , pp. 367-373, MIT
Press, 2002.
32. J. Kandola and J. Shawe-taylor, “Refining kernels for regression and uneven classifi-
cation problems,” in Proceedings of International Conference on Artificial Intelligence
and Statistics , Springer-Verlag, 2003.
33. C.-Y. Yang, J.-S. Yang, and J.-J. Wang, “Margin calibration in SVM class-imbalanced
learning,” Neurocomputing , vol. 73, no. 1-3, pp. 397-411, 2009.
34. X. Hong, S. Chen, and C. Harris, “A kernel-based two-class classifier for imbalanced
data sets,” IEEE Transactions on Neural Networks , vol. 18, no. 1, pp. 28-41, 2007.
35. A. Qin and P. Suganthan, “Kernel neural gas algorithms with application to cluster
analysis,” in Proceedings of the 17th International Conference on Pattern Recognition
(Cambridge, UK), pp. 617-620, IEEE Computer Society, 2004.
36. X.-P. Yu and X.-G. Yu, “Novel text classification based on k-nearest neighbor,” in
Proceedings of the International Conference on Machine Learning and Cybernetics
(Hong Kong, China), pp. 3425-3430, 2007.