1 |
Ederington, H. L., "Classification models and bond ratings", Financial Review, Vol.20, No.4(1985), 237-262.
DOI
|
2 |
Fawcett, T. and F. Provost, "Adaptive fraud detection", Data Mining and Knowledge discovery, Vol.1, No.3(1997), 291-316.
DOI
ScienceOn
|
3 |
Fisher, L., "Determinants of risk premiums on corporate bonds", Journal of Political Economy, Vol.67(1959), 217-237.
DOI
ScienceOn
|
4 |
Freund, Y. and R. E. Schapire, "A decision theoretic generalization of online learning and an application to boosting", Journal of Computer and System Science, Vol.55, No.1(1997), 119- 139.
DOI
ScienceOn
|
5 |
Gentry, J. A., Whitford, D. T., and P. Newbold, "Predicting industrial bond ratings with a probit model and funds flow components", Financial Review, Vol.23, No.3(1988), 269-286.
DOI
|
6 |
Hong, X., "A kernel-based two-class classifier for imbalanced data sets", IEEE Transactions on neural networks, Vol.18, No.1(2007), 28-40.
DOI
|
7 |
Hsu, C. W. and C. J. Lin, "A Comparison of Methods for Multiclass Support Vector Machines", IEEE Transactions on Neural Networks, Vol.13, No.2(2002), 415-425.
DOI
ScienceOn
|
8 |
Huang, Zan, Chen, Hsinchun, Hsu, Chia-Jung, Chen, Wun-Hwa, and Wu, Soushan, "Credit rating analysis with support vector machines and neural networks. A market comparative study", Decision Support Systems, Vol.37(2004), 543-558.
DOI
ScienceOn
|
9 |
Jackson, J. D. and J. W. Boyd, "A statistical approach to modeling the behavior of bond raters", The Journal of Behavioral Economics, Vol.17, No.3(1988), 173-193.
DOI
ScienceOn
|
10 |
Kim, K., "Financial time series forecasting using support vector machines", Neurocomputing, Vol.55(2004), 307-319.
|
11 |
Kotsiantis, S., D. Tzelepis, E. Kounmanakos, and V. Tampakas, "Selective costing voting for bankruptcy prediction", International Journal of Knowledge-based and Intelligent Engineering Systems, Vol.11(2007), 115-127.
DOI
|
12 |
Kubat, M., Holte, R., and S. Matwin, "Learning when Negative example abound", Proceedings of the 9th European Conference on Machine Learning, ECML'97, 1997.
|
13 |
Kwon, Y. S., Han, I. G., and K. C. Lee, "Ordinal Pairwise Partitioning (OPP) approach to neural networks training in bond rating", Intelligent Systems in Accounting, Finance and Management, Vol.6(1997), 23-40.
DOI
ScienceOn
|
14 |
Maher, J. J. and T. K. Sen, "Predicting bond ratings using neural networks : A comparison with logistic regression", Intelligent Systems in Accounting, Finance and Management, Vol.6(1997), 59-72.
DOI
ScienceOn
|
15 |
Maia, T. T., A. P. Braga, and A. F. Carvalho, "Hybrid classification algorithms based on boosting and support vector machines", Kybernetes, Vol.37, No.9(2008), 1469-1491.
DOI
|
16 |
Min, S. H., J. M. Lee, and I. G. Han, Hybrid genetic algorithms and support vector machines for bankruptcy prediction, Expert Systems with Applications, Vol.31(2006), 652-660.
DOI
ScienceOn
|
17 |
Optiz, D. and R. Maclin, "Popular ensemble methods : an empirical study", Journal of Artificial Intelligence, Vol.11(1999), 169-198.
|
18 |
Pinches, G. E. and K. A. Mingom, "A multivariate analysis of industrial bond ratings", Journal of Finance, Vol.28, No.1(1973), 1-18.
DOI
ScienceOn
|
19 |
Platt, J., "Fast Training of Support Vector Machines using Sequential Minimal Optimization. In B. Schoelkopf, C. Burges, and A. Smola, (Eds.)", Advances in Kernel MethodsSupport Vector Learning, MIT Press, 1998.
|
20 |
Pogue, T. F. and R. M. Soldofsky, "What's in a bond rating?", Journal of Financial and Quantitative Analysis, Vol.4, No.2(1969), 201-228.
DOI
ScienceOn
|
21 |
Seiffert, C., T. M. Khoshgoftaar, J. Van Hulse, and A. Napolitano, "RUSBoost : Improving classification performance when training data is skewed", 19th International Conference on Pattern Recognition, (2008), 1-4.
|
22 |
Shin, H. J. and S. Z. Cho, "Response modeling with support vector machines", Expert Systems with applications, Vol.30, No.4(2006), 746-760.
DOI
ScienceOn
|
23 |
Shin, K., T. Lee, and H. Kim, "An application of support vector machines in bankruptcy prediction", Expert Systems with Applications, Vol.28(2005), 127-135.
DOI
ScienceOn
|
24 |
Tay, F. E. J. and L. J. Cao, "Modified support vector machine in financial time series forecasting", Neurocomputing, Vol.48(2002), 847-861.
DOI
|
25 |
Vapnik, V. N., "The nature of statistical learning theory", New York:Springer, 1995.
|
26 |
Wang, B. X. and N. Japkowicz, "Boosting support vector machines for imbalanced data sets", Knowledge and Information Systems, Vol.25(2010), 1-10.
DOI
ScienceOn
|
27 |
Weiss, G. M., "Mining with rarity : A unifying framework", SIGKDD Explorations, Vol.T, No.1(2004), 7-19.
|
28 |
West, R. R., "An alternative approach to predicting corporate bond ratings", Journal of Accounting Research, Vol.8, No.1(1970), 118-125.
DOI
ScienceOn
|
29 |
Wu, G. and E. Chang, "Adaptive feature-space conformal transformation for imbalanced data learning", Proceedings of the 20th International Conference on Machine Learning, 2003.
|
30 |
Wu, G. and E. Chang, "KBA : Kernel boundary alignment considering imbalanced data distribution", IEEE Transactions on knowledge and data engineering, Vol.17, No.6(2005), 786- 795.
DOI
|
31 |
Wu, G. Y. Wu, L. Jiao, Y. F. Wang, and E. Chang, "Multi-camera spatio-temporal fusion and biased sequence-data learning for security surveillance", Proceedings of 20th International Conference on Multimedia, 2003.
|
32 |
강필성, 조성준, "데이터 불균형 해결을 위한 Undersampling 기반 앙상블 SVMs", 대한산업공학회/한국경영과학회 2006 춘계공동학술대회, 2006.
|
33 |
김명종, "기업부실 예측 데이터의 불균형 문제 해결을 위한 앙상블 학습", 지능정보연구, 2009, 15권 3호(2009), 1-15.
|
34 |
신택수, 홍태호, "AdaBoost 알고리즘 기반 SVM을 이용한 부실 확률분포 기반의 기업신용평가", 지능정보연구, 17권 3호(2011), 25-41.
|
35 |
안현철, 김경재, 한인구, "다분류 Support Vector Machine을 이용한 한국기업의 지능형 기업채권평가모형", 경영학연구, 35권 5호(2006), 1479-1496.
|
36 |
옥중경, 김경재, "유전자 알고리즘 기반의 기업부실예측 통합모형", 지능정보연구, 15권 4호(2009), 99-121.
|
37 |
Bruzzone, L. and S. B. Serpico, "Classifications of imbalanced remote-sensing data by neural networks", Pattern recognition letters, Vol.18, No.11-13(1997), 1323-1328.
DOI
ScienceOn
|
38 |
Cao, L. and F. E. H. Tay, "Financial forecasting using support vector machines", Neural Computing and Applications, Vol.10(2001), 184- 192.
DOI
ScienceOn
|
39 |
Chawla, N., A. Lazarevic, L. Hall, and K. Bowyer, "SMOTEBoost : Improving prediction of the minority class in boosting", 7th European conference on principles and practice of knowledge discovery in databases(2003), Cavtatv Dubrovnik, Croatia, 107-119.
|
40 |
Chaveesuk, R., Srivaree-Ratana, C., and A. E. Smith, "Alternative neural network approaches to corporate bond rating", Journal of Engineering Valuation and Cost Analysis, Vol.2, No.2(1993), 117-131.
|
41 |
Cover, T. M. and J. A. Thomas, Element of information theory, John Wiley and Sons, 1991.
|
42 |
Darbellay, G. A., "An estimator of the mutual information based on a criterion for independence", Computational Statistics and Data Analysis, Vol.32(1999), 1-17.
DOI
ScienceOn
|
43 |
Dutta, S. and S. Shekhar, "Bond rating : A non-conservative application application of neural networks", Proceedings of IEEE International Conference on Neural Networks, (1988), II443-II450.
|