참고문헌
- Beaver, W, "Financial ratios as predictors of failure, empirical research in accounting: Selected studied", Journal of Accounting Research, 1966, pp.71-111.
- Altman, E. L, "Financial ratios, discriminant analysis and the prediction of corporate bankruptcy", The Journal of Finance, 23(3), 1968, pp.589-609. https://doi.org/10.1111/j.1540-6261.1968.tb00841.x
- Altman, E. L., Edward, I., Haldeman, R., & Narayanan, P. A, "new model to identify bankruptcy risk of corporations", Journal of Banking and Finance, 1, 1977, pp.29-54. https://doi.org/10.1016/0378-4266(77)90017-6
- Meyer, P. A., & Pifer, H, "Prediction of bank failures", The Journal of Finance, 25, 1970, pp.853-868. https://doi.org/10.1111/j.1540-6261.1970.tb00558.x
- Dimitras, A. I., Zanakis, S. H., & Zopounidis, C, "A survey of business failure with an emphasis on prediction methods and industrial applications", European Journal of Operational Research, 90(3), 1996 , pp.487-513. https://doi.org/10.1016/0377-2217(95)00070-4
- Ohlson, J, "Financial ratios and the probabilistic prediction of bankruptcy", Journal of Accounting Research, 18(1),1980 , pp.109-131. https://doi.org/10.2307/2490395
- Pantalone, C., & Platt, M. B, "Predicting commercial bank failure since deregulation", New England Economic Review, 1987, pp.37-47.
- Han, I., Chandler, J. S., & Liang, T. P, "The impact of measurement scale and correlation structure on classification performance of inductive learning and statistical methods", Expert System with Applications, 10(2), 1996, pp.209-221. https://doi.org/10.1016/0957-4174(95)00047-X
- Shaw, M., & Gentry, J, "Using and expert system with inductive learning to evaluate business loans", Financial Management, 17(3), 1998, pp.45-56.
- Buta, P, "Mining for financial knowledge with CBR", AI Expert, 9(10), 1994, pp.34-41.
- Bryant, S. M, "A case-based reasoning approach to bankruptcy prediction modeling", International Journal of Intelligent Systems in Accounting, Finance and Management, 6(3), 1997, pp.195-214. https://doi.org/10.1002/(SICI)1099-1174(199709)6:3<195::AID-ISAF132>3.0.CO;2-F
- Bortiz, J. E., & Kennedy, D. B, "Effectiveness of neural network types for prediction of business failure", Expert Systems with Application, 9(4), 1995, pp.503-512. https://doi.org/10.1016/0957-4174(95)00020-8
- Zhang, G., Hu, M. Y., Patuwo, B. E., & Indro, D. C, "Artificial neural networks in bankruptcy prediction: General framework and crossvalidation analysis", European Journal of Operational Research, 116(1), 1999 , pp.16-32. https://doi.org/10.1016/S0377-2217(98)00051-4
- Coakley, J. R., & Brown, C. E, "Artificial neural networks in accounting and finance: Modeling issues", International Journal of Intelligent Systems in Accounting, Finance and Management, 9(2), 2000, pp.119-144. https://doi.org/10.1002/1099-1174(200006)9:2<119::AID-ISAF182>3.0.CO;2-Y
- Fan, A., & Palaniswami, M, "Selecting bankruptcy predictors using a support vector machine approach", Proceeding of the international joint conference on neural network, Vol. 6, 2000, pp. 354-359.
- Van Gestel, T., Baesens, B., Suykens, J., Espinoza, M. Baestaens, D.-E., Vanthienen, J., et al. "Bankruptcy prediction with least squares support vector machine classifiers, computational intelligence for financial engineering", 2003, proceeding 2003. IEEEE international conference on 2003, pp.1-8.
- Min, S.,& Lee, J., "Hybrid genetic algorithms and support vector machines for bankruptcy prediction", Expert Systems with Applications, Volume 31, Issue 3, October 2006, pp.652-660. https://doi.org/10.1016/j.eswa.2005.09.070
- 신택수, 홍태호, "AdaBoost 알고리즘 기반 SVM을이용한 부실 확률분포 기반의 기업신용평가", 지능정보연구, 17권 3호(2011), pp.25-41.
- 김명종, "유전자 알고리즘을 이용한 분류기 앙상블의 최적 선택", 지능정보연구 제16권 제4호 2010, pp. 99-112.
- 김승혁, 김종우, "Modified Bagging Predictors를 이용한 SOHO 부도 예측", 한국지능정보시스템학회논문지 제13권 제2호, 2007, pp.15-26.
- Dietterich, T. G, "Machine-learning research: Four current directions", AI Magazine, 18(4), 1997, pp.97-136.
- Kuncheva L.I, "Combining classifiers: Soft computing solutions", in: S.K. Pal and A. Pal (Eds.) Pattern Recognition: From Classical to Modern Approaches, World Scientific Publishing Co., Singapore, 2001, 427-452
- Breiman, L, "Bagging predictors", Machine Learning, 24(2), 1996, pp.123-140.
- Vapnik, V. N, "The nature of statistical learning theory", New York: Springer, 1995.
- 박창석, 김병만, 서병훈, 김준우,이광호,"이동 차량에서의 실시간 자동차 번호판 인식",한국산업정보학회논문지, v.9, no.2, 2004년, pp.32-43
- 원철호, 이상헌, 이태균,"인터랙티브 TV 컨트롤 시스템을 위한 근적외선 영상의 얼굴 인식", 한국산업정보학회논문지, v.15, no.5, 2010년, pp.11-17
- 유혜경, 이진영, 나종화,"매장문화재 예측을 위한 통계적 분류 분석", 한국산업정보학회논문지, v.14, no.3, 2009년, pp.106-113
- Ho, T. K, "The random subspace method for constructing decision forests", IEEE Transactions on Pattern Analysis and Machine Intelligence, 20(8), 1998, pp.832-844. https://doi.org/10.1109/34.709601
피인용 문헌
- Investigating Dynamic Mutation Process of Issues Using Unstructured Text Analysis vol.22, pp.1, 2016, https://doi.org/10.13088/jiis.2016.22.1.139
- Improving an Ensemble Model by Optimizing Bootstrap Sampling vol.17, pp.2, 2016, https://doi.org/10.7472/jksii.2016.17.2.49
- Bankruptcy prediction using an improved bagging ensemble vol.20, pp.4, 2014, https://doi.org/10.13088/jiis.2014.20.4.121
- 유전자 알고리즘 기반 통합 앙상블 모형 vol.23, pp.1, 2012, https://doi.org/10.21219/jitam.2016.23.1.045