참고문헌
- Marques, A. I., Garcia, V., and Sanchez, J. S. (2013), "On the suitability of resampling techniques for the class imbalance problem in credit scoring", Journal of the Operational Research Society, 64(7), 1060-1070. https://doi.org/10.1057/jors.2012.120
- Thomas, L. C., Edelman, D. B., and Crook, L. N. (2002), Credit Scoring and I ts Applications, Philadelphia: Society for Industrial and Applied Mathematics.
- West, D. (2002), "Neural network credit scoring models", Computers and Operations Research, 27(12), 1131-1152.
- Guardia, N. (2002), "Consumer credit in the European Union", ECRI Research Report 1, 1-39.
- Richard, D., and John, G. (2013), "Financial literacy and consumer credit portfolios", Journal of Banking & Finance, 37(7), 2246-2254. https://doi.org/10.1016/j.jbankfin.2013.01.013
- Fisher, R. A., "The Use of Multiple Measurements in Taxonomic Problems", Annals of Eugenics, Vol. 7, No. 2, 1936, pp. 179-188. https://doi.org/10.1111/j.1469-1809.1936.tb02137.x
- West, D. (2000), "Neural network credit scoring models", Computers & Operations Research, 27(12), 1131-1152. https://doi.org/10.1016/S0305-0548(99)00149-5
- Pavlidis, N., Tasoulis, D., Adams, N., and Hand, D. (2012), "Adaptive consumer credit classification", Journal of the Operational Research Society, 63(12), 1645-1654. https://doi.org/10.1057/jors.2012.15
- Yap, B., Ong, S., and Husain, N. (2011), "Using data mining to improve assessment of credit worthiness via credit scoring models", Expert Systems with Applications, 38(10), 13274-13283. https://doi.org/10.1016/j.eswa.2011.04.147
- Cock, M. D., Dowsley, R., Horst, C., Katti, R., Nascimento, A., & Poon, W. S. (2017)., "Efficient and private scoring of decision trees, support vector machines and logistic regression models based on pre-computation", IEEE Transactions on Dependable & Secure Computing, 16(2), 217-230.
- Ripley, B. D. (1996), Pattern Recognition and Neural Networks, Cambridge University Press.
- Abdou, H., Pointon, J., and El-Masry, A. (2008), "Neural nets versus conventional techniques in credit scoring in egyptian banking", Expert Systems with Applications, 35(3), 1275-1292. https://doi.org/10.1016/j.eswa.2007.08.030
- Marcano-Cedeno, A., Marin-De-La-Barcena, A., Jimenez-Trillo, J., Pinuela, J., and Andina, D. (2011), "Artificial metaplasticity neural network applied to credit scoring", International Journal of Neural Systems, 21(4), 311-317. https://doi.org/10.1142/S0129065711002857
- Pang, S.-L. (2005), "Study on credit scoring model and forecasting based on probabilistic neural network", System Engineering Theory and Practice, 25(5), 43-48.
- Ayouche, S., Aboulaich, R., & Ellaia, R. (2017). "Partnership credit scoring classification problem: a neural network approach", International Journal of Applied Engineering Research, 12(5), 693-704.
- Chi, G., Abedin, MZ., and Fahmida, E.M. (2017), "Chinese Small Business Credit Scoring: Application of Multiple Hybrids Neural Network", International Journal of Database Theory and Application, 10(2), 1-22. https://doi.org/10.14257/ijdta.2017.10.2.01
- Cristianini, N., and Shawe-Taylor, J. (2000), An introduction to support vector machines, Cambridge, England: Cambridge University Press.
- Gunn, S. R. (1998), "Support vector machines for classification and regression", Technical Report, University of Southampton.
- Hearst, M. A., Dumais, S. T., Osman, E., Platt, J., and Scholkopf, B. (1998), "Support vector machines", IEEE Intelligent System, 13(4), 18-28.
- Vapnik, V. (1998), Statistical learning theory, New York: Springer.
- Lee, Y. C. (2006), "Application of support vector machines to corporate credit rating prediction", Expert Systems with Applications, 33(1), 67-74. https://doi.org/10.1016/j.eswa.2006.04.018
- Chen, W., Ma, C., and Ma, L. (2009), "Mining the customer credit using hybrid support vector machine technique", Expert Systems with Applications, 36(4), 7611-7616. https://doi.org/10.1016/j.eswa.2008.09.054
- Zhou, L., Lai, K., Yu, L. (2010), "Least squares support vector machines ensemble models for credit scoring", Expert Systems with Applications, 37(1), 127-133. https://doi.org/10.1016/j.eswa.2009.05.024
- Li, Z., Tian, Y., Li, K., Zhou, F., and Yang, W. (2017), "Reject inference in credit scoring using semi-supervised support vector machines", Expert Systems with Applications, 74, 105-114. https://doi.org/10.1016/j.eswa.2017.01.011
- Shi, J., and Xu, B., "Credit scoring by fuzzy support vector machines with a novel membership function", Journal of Risk and Financial Management, 9(4), 13-23. https://doi.org/10.3390/jrfm9040013