Multiclass Corporate Failure Prediction by Adaboost.M1
Tóm tắt
Từ khóa
Tài liệu tham khảo
Alfaro, E., Gámez, M., & García, N. (2006). adabag: implements adaboost.M1 and bagging. R package version 1.0. http://www.R-project.org
Altman, E. I. (1968). Financial ratios, discriminant analysis and the prediction of corporate bankruptcy. Journal of Finance, 23(4), 589–609.
Bauer, E., & Kohavi, R. (1999). An empirical comparison of voting classification algorithm: bagging, boosting and variants. Machine Learning, 36, 105–142.
Beaver, W. H. (1966). Financial ratios as predictors of failure. Empirical research in accounting: selected studies. Journal of Accounting Research, 4(Supplement), 71–111.
Breiman, L., Friedman, J. H., Olshen, R., & Stone, C. J. (1984). Classification and regression trees. Belmont: Wadsworth International Group.
Dietterich, T. G. (2000). Ensemble methods in machine learning. In J. Kittler, & F. Roli (Eds.). Multiple Classifier Systems, vol. 1857 of Lecture Notes in Computer Science (pp. 1–15). New York: Springer.
Freund, Y., & Schapire, R. E. (1996). Experiments with a new boosting algorithm. In Proceedings of the 13th International Conference on Machine Learning (pp. 148–156). Bari, Italy.
Freund, Y., & Schapire, R. E. (1997). A decision-theoretic generalization of on-line learning and an application to boosting. Journal of Computer and System Sciences, 55(1), 119–139.
Friedman, J., Hastie, T., & Tibshirani, R. (2000). Additive logistic regression: A statistical view of boosting. Annals of Statistics, 38(2), 337–374.
Frydman, H., Altman, E., & Kao, D. (1985). Introducing recursive partitioning for financial classification: The case of financial distress. Journal of Finance, 40(1), 269–291.
Ohlson, J. A. (1980) Financial ratios and the probabilistic prediction of bankruptcy. Journal of Accounting Research, 18(1), 5–12.
R Development Core Team (2004). R: A language and environment for statistical computing. Viena: R Foundation for Statistical Computing. http://www.R-project.org
Ripley, B. D. (2004). Rpart: Recursive Partitioning. R package version 3.1-20. http://www.R-project.org
Valentini, G., & Masulli, F. (2002). Ensembles of learning machines. In M. Marinaro, & R. Tagliaferri (Eds). Proceedings of the 13th Italian Workshop on Neural Nets, vol. 2486 of Lecture Notes in Computer Science (pp. 3–19) Berlin Heidelberg New York: Springer.
Wilson, R. L., & Sharda, R. (1994). Bankruptcy prediction using neural network. Decision Support Systems, 11(5), 545–557.