참고문헌
- Meyer, P. A., & Pifer, H. (1970). Prediction of bank failures. The Journal of Finance, 25, 853-868. https://doi.org/10.2307/2325421
- Altman, E. L. (1968). Financial ratios, discriminant analysis and the prediction of corporate bankruptcy. The Journal of Finance, 23(4), 589-609. https://doi.org/10.2307/2978933
- Altman, E. L., Edward, I., Haldeman, R., & Narayanan, P. (1977). A new model to identify bankruptcy risk of corporations. Journal of Banking and Finance, 1, 29-54. https://doi.org/10.1016/0378-4266(77)90017-6
- Dimitras, A. I., Zanakis, S. H., & Zopounidis, C. (1996). A survey of business failure with an emphasis on prediction methods and industrial applications. European Journal of Operational Research, 90(3), 487-513. https://doi.org/10.1016/0377-2217(95)00070-4
- Ohlson, J. (1980). Financial ratios and the probabilistic prediction of bankruptcy. Journal of Accounting Research, 18(1), 109-131. https://doi.org/10.2307/2490395
- Pantalone, C., & Platt, M. B. (1987). Predicting commercial bank failure since deregulation. New England Economic Review, 37-47.
- Zmijewski, M. E. (1984). Methodological issues related to the estimation of financial distress prediction models. Journal of Accounting Research, 22(1), 59-82. https://doi.org/10.2307/2490859
- Han, I., Chandler, J. S., & Liang, T. P. (1996). The impact of measurement scale and correlation structure on classification performance of inductive learning and statistical methods. Expert System with Applications, 10(2), 209-221. https://doi.org/10.1016/0957-4174(95)00047-X
- Shaw, M., & Gentry, J. (1998). Using and expert system with inductive learning to evaluate business loans. Financial Management, 17(3), 45-56.
- Bryant, S. M. (1997). A case-based reasoning approach to bankruptcy prediction modeling. International Journal of Intelligent Systems in Accounting, Finance and Management, 6(3), 195-214
- Buta, P. (1994). Mining for financial knowledge with CBR. AI Expert, 9(10), 34-41.
- Laitinen, T., & Kankaanpaa, M. (1999) Comparative analysis of failure prediction methods: the Finish case. European Accounting Review, 8(1), 67-92. https://doi.org/10.1080/096381899336159
- Odom, M., & Sharda, R. (1990). A neural network for bankruptcy prediction, Proceedings of the International Joint Conference on Neural Networks, IEEE Press, San Diego, CA.
- Ravi, P., & Ravi, K. V. (2007). Bankruptcy prediction in banks and firms via statistical and intelligent techniques-a review. European Journal of Operational Research, 180, 1-28. https://doi.org/10.1016/j.ejor.2006.08.043
- Min, S. H.; Lee, J. M., & Han, I. G. (2006). Hybrid genetic algorithms and support vector machines for bankruptcy prediction. Expert Systems with Applications, 31, 652-660. https://doi.org/10.1016/j.eswa.2005.09.070
- Shin, K., Lee, T., & Kim, H. (2005). An application of support vector machines in bankruptcy prediction. Expert Systems with Applications, 28, 127-135. https://doi.org/10.1016/j.eswa.2004.08.009
- Alfaro, E., Gamez, M., & Garcia, N. (2007). Multiclass corporate failure prediction by AdaBoost.M1. Advanced Economic Research, 13, 301-312. https://doi.org/10.1007/s11294-007-9090-2
- Alfaro, E., García, N., Gámez, M., & Elizondo, D. (2008). Bankruptcy forecasting: an empirical comparison of AdaBooost and neural networks. Decision Support Systems, 45, 110-122. https://doi.org/10.1016/j.dss.2007.12.002
- Kim, M. J., (2009). A Performance Comparison of Ensembles in Bankruptcy Prediction, Entrue Journal of Information Technology, 8(2), 41-49.
- Perrone, M. E. (1994). Putting it all together: Methods for combining neural networks. In J. D. Cowan, G. Tesauro, & J. Alspector, (Eds.), Advances in Neural Information Processing Systems, 6, (pp. 1188-1189). San Mateo, CA: Morgan Kaufmann.
- Schapire, R. E. (1990). The strength of weak learnability. Machine Learning, 5(2), 197-227.
- Buciu, I., Kotrooulos, C., & Pitas, I. (2001). Combining support vector machines for accuracy face detection, Proc. ICIP, 1054-1057.
- Dong, Y. S., & Han, K. S. (2004). A comparison of several ensemble methods for text categorization, IEEE International Conference on Service Computing.
- Eom, J. H., Kim, S. C. & Zhang, B. T. (in press). AdaaCDSS-E: A classifier ensemble based clinical decision support systems for cardiovascular disease level prediction, Expert Systems with Applications.
- Valentini, G., Muselli, M. & Ruffino, F. (2003). Bagged ensembles of SVMs or gene expression data analysis, The IEEE-INNS-ENNS International Joint Conference on Neural Networks, 1844-1849
- Breiman, L. (1994). Bagging predictors. Machine Learning, 24(2), 123-140.
- Freund, Y. (1995). Boosting a weak learning algorithm by majority. Information and Computation, 121(2), 256-285. https://doi.org/10.1006/inco.1995.1136
- Banfield, R. E., Hall, L. O., Bowyer, K. W., & Kegelmeyer, W. P. (2007). A comparison of decision tree ensemble creation techniques. IEEE Transactions on Pattern Analysis and Machine Intelligence, 29(1), 173-180. https://doi.org/10.1109/TPAMI.2007.250609
- Bauer, E., & Kohavi, R. (1999). An empirical comparison of voting classification algorithms: Bagging, boosting, and variants. Machine Learning, 36, 105-139. https://doi.org/10.1023/A:1007515423169
- Drucker, H., & Cortes, C. (1996). Boosting decision trees, Advanced Neural Information Processing Systems, 8.
- Quinlan, J. R. (1996). Bagging, boosting and C4.5. Machine Learning: Proceedings of the Fourteenth International Conference , 725-730).
- Valentini, G., & Dietterich, T. (2004). Bias-variance analysis of support vector machines for the development of SVM-based ensemble methods, Journal of Machine Learning Research, 5, 725-775.
- Valentini, G. (2005). An experimental bias-variance analysis of SVM ensembles based on resampling techniques. IEEE Transactions on Systems, Man and Cybernetics-Part B: Cybernetics, 35(6), 1252-1271. https://doi.org/10.1109/TSMCB.2005.850183
- Pang, S., Kim, D., & Sung, Y. (2003). Membership authentication in the dynamic group by face classification using SVM ensemble, Pattern Recognition Letter, 24, 215-225. https://doi.org/10.1016/S0167-8655(02)00213-1
- Pang, S., Kim, D., & Sung, Y. (2003). Membership authentication in the dynamic group by face classification using SVM ensemble, Pattern Recognition Letter, 24, 215-225. https://doi.org/10.1016/S0167-8655(02)00213-1
- Gordon, J. J., Towsey, M. W., Hogan, J. M., Mathews S. A., & Timms, P. (2006). Improved prediction of bacterial transcription start sites, Bioinformatics, 22(2) 142-148 https://doi.org/10.1093/bioinformatics/bti771
- Hu, Q., He, Z., Zhang, Z., and Zi, Y. (2007). Fault diagnosis of rotating machinery based on improved wavelet package transform and SVMs ensemble. Mechanical Systems and Signal Processing, 21, 688-705. https://doi.org/10.1016/j.ymssp.2006.01.007
- Lei, Z, Yang, Y., & Wu, Z.(2006). Ensemble of support vector machine for text-independent speaker recognition, International Journal of Computer Science and Network Security 6(5A) 163-167.
- Wang, Y. Q. (2008). Building credit scoring systems based on support-based support vector machine ensemble, Fourth International Conference on Natural Computation, 323-326.
- Hansen, L., & Salamon, P. (1990). Neural network ensembles, IEEE Trans. PAMI, Vol. 12, 993-1001. https://doi.org/10.1109/34.58871
- Ho, T. K. (2002). Multiple classifier combination: lessons and next steps, in Hybrid Methods in Patter Recognition. (Ed. By H. Bubke & A. Kandel), World Scientific, 2002.
- Zhou, Z. H. Wu, J. X., & Tang, W. (2002). Ensembling neural networks: many could better than all, Artificial Intelligence, 137, 239-263. https://doi.org/10.1016/S0004-3702(02)00190-X
- Oliveira, L. S., Sabourin, R., Bortolozzi, F., & Suen, C.Y. (2003). Feature selection for ensembles: a hierarchical multi-objective genetic algorithm approach, ICDAR 2003.
- Optiz, D., & Maclin, R. (1999). Popular ensemble methods: an empirical study. Journal of Artificial Intelligence, 11, 169-198.
- Maia, T. T., Braga, A. P., & Carvalho, A. F. (2008). Hybrid classification algorithms based on boosting and support vector machines, Kybernetes, 37(9), 1469-1491 https://doi.org/10.1108/03684920810907814
피인용 문헌
- Illumination correction of dyed fabrics method using rotation forest-based ensemble particle swarm optimization and sparse least squares support vector regression pp.03612317, 2018, https://doi.org/10.1002/col.22262
- 딥러닝 시계열 알고리즘 적용한 기업부도예측모형 유용성 검증 vol.24, pp.4, 2010, https://doi.org/10.13088/jiis.2018.24.4.001