References
- S. Abe, Analysis of support vector machines, Neural Networks for Signal Processing XII - Proceedings of the 2002 IEEE Signal Processing Society Workshops, Centre du Parc, Martigny, Switzerland, 2002, pp. 89–98.
- S. Abe, Support Vector Machines for Pattern Classification. 2nd ed., Springer, London, 2010.
- R. Akbani, S. Kwek, and N. Japkowizc, Applying support vector machines to imbalanced datasets. In Proceedings of the 15th European Conference Machine Learning, J.F. Boulicaut, F. Esposito, F. Giannotti, and D. Pedreschi, eds., Springer-Verlag, Berlin, Heidelberg, 2004, pp. 39–50.
- C.J.C. Burges, A tutorial on support vector machines for pattern recognition, Data Min. Knowl. Discovery 2(2) (1998), pp. 121–167. doi: 10.1023/A:1009715923555
- N.V. Chawla, K.W. Bowyer, L.O. Hall, and W.P. Kegelmeyer, SMOTEL synthetic minority over-sampling technique, J. Artif. Intell. Res. 16 (2002), pp. 321–357.
- N.V. Chawla, N. Japkowicz, and A. Kolcz, Editorial: special issue on learning from imbalanced data sets, SIGKDD Explorations Special Issue on Learning from Imbalanced Datasets 6 (1) (2004), pp. 1–6.
- V. Cherkassky and F. Mulier, Learning from Data-Concepts, Theory and Methods, John Wiley & Sons, New York, 1998.
- C. Cortes and V. Vapnik, Support vector networks, Mach. Learn. 20 (1995), pp. 1–25.
- N. Cristianini and J. Shawe-Taylor, An Introduction to Support Vector Machines and Other Kernel-Based Learning Methods, Cambridge University Press, Cambridge, 2000.
- R.A. Fisher, Has Mendel's work been rediscovered? Ann. Sci. 1 (1936), pp. 115–137. doi: 10.1080/00033793600200111
- G. Fung and O.L. Mangasarian, Proximal support vector machine classifiers, in Proceedings KDD-2001: Knowledge Discovery and Data Mining, F. Provost and R. Srikant, eds., San Francisco, CA, Association for Computing Machinery, New York, 2001, pp. 77–86.
- G. Fung and O.L. Mangasarian, Incremental support vector machine classification, in Proceedings of the Second SIAM International Conference on Data Mining, R. Grossman, H. Manilla and R. Motwani, eds., Arlington, VA, 2002, pp. 247–260.
- T. Hastie, R. Tibshirani, and J.J.H. Friedman, Elements of Statistical Learning, 2nd ed., Springer, New York, 2009.
- T. Imam, K.M. Ting, and J. Kamruzzaman, z-SVM: An SVM for improved classification of imbalanced data, in Advances in Artificial Intelligence, A. Sattar and B.H. Kang, eds., Springer-Verlag, Berlin, Heidelberg, 2006, pp. 264–273.
- M. Kubat and S. Matwin, Addressing the curse of imbalanced training sets: one-sided selection, Proceedings of the 14th International Conference on Machine Learning, Morgan Kaufmann Publishers, San Francisco, CA, USA, 1997, pp. 179–186.
- B. Li, A. Artemiou, and L. Li, Principal support vector machines for linear and nonlinear sufficient dimension reduction, Ann. Stat. 39 (2011), pp. 3182–3210. doi: 10.1214/11-AOS932
- M.S. Pepe, Receiver operating characteristic methodology, J. Amer. Statist. Assoc. 95 (2000), pp. 308–11. doi: 10.1080/01621459.2000.10473930
- X.-Y. Tao, H.-B. Ji, and Y.-X. Xie, A modified PSVM and its application to unbalanced data classification, Third International Conference on Natural Computation (ICNC 2007), Publisher IEEE, Haikou, China, 2007, pp. 488–490.
- N.V. Vapnik, Statistical Learning Theory, John Wiley & Sons, Inc., New York, 1998.
- V.N. Vapnik, The Nature of Statistical Learning Theory, 2nd ed., Springer, Berlin, Heidelberg, 2000.
- K. Veropoulos, C. Campbell, and N. Cristianini, Controlling the sensitivity of support vector machines, Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence (IJCAI ’99), Workshop ML3, Stockholm, Sweden, 1999, pp. 55–60.
- R. Yan, Y. Liu, R. Jin, and A. Hauptmann, On predicting rare classes with svm en-sembles in scene classification, Proceedings of the 2003 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP ’03) 3, 2003, III– 21–24.
- W. Zhou, L. Zhang, and L. Jiao, Linear programming support vector machines, Pattern Recognit. 35 (2002), pp. 2927–2936. doi: 10.1016/S0031-3203(01)00210-2