財務危機預測的問題長久以來都是一個重要且常被廣泛討論的主題，吸引了世界各地的投資者和研究學者的關注。發展出好的財務危機預警模型可以有效幫助，投資者銀行決策。影響整個財務危機預警流程主要有三個議題分別是特徵挑選(Feature selection)、分類器演算法(Classifier algorithm)和資料集(Dataset)。過去由前人的研究可發現，是否要做特徵挑選方法會依據分類器的特性來判定。本研究專注在以準確率和Type I作為指標，探討常用的分類器是否需要做特徵挑選，最後推薦一套方法針對未來資料集可以縮小搜尋的範圍和時間。

Financial distress problem has been important and widely studied topic. Financial distress prediction is receiving increasing attention of stakeholders and researchers in the worldwide. It is helpful for stakeholders and researchers that developing a great financial distress prediction model. There are three important factors influencing financial distressed prediction. The first one is feature selection, the second one is classifier algorithm, and the third one is dataset. It can find that it is useful to use feature selection in different kinds of datasets in previous studies. The aim of this research is make sure that what kinds of classifier need to use feature selection to have the better accuracy, and also recommend a way that it can reduce a lots of time to search the better combination of feature selection approach and classifier in the new datasets.

[1] W.-Y. Lin, Y.-H. Hu, and C.-F. Tsai, "Machine learning in financial crisis prediction: a survey," Systems, Man, and Cybernetics, Part C: Applications and Reviews, IEEE Transactions on, vol. 42, pp. 421-436, 2012.
[2] P. J. Fitzpartrick, "A comparison of ratios of successful industrial enterprises with those of failed companies," Journal of Accounting Research, pp. 598-605, 1932.
[3] W. H. Beaver, "Financial ratios as predictors of failure," Journal of accounting research, pp. 71-111, 1966.
[4] E. I. Altman, "Financial ratios, discriminant analysis and the prediction of corporate bankruptcy," The journal of finance, vol. 23, pp. 589-609, 1968.
[5] J. A. Ohlson, "Financial ratios and the probabilistic prediction of bankruptcy," Journal of accounting research, vol. 18, pp. 109-131, 1980.
[6] E. S. Pearson, W. S. Gosset, R. L. Plackett, and G. A. Barnard, Student: a statistical biography of William Sealy Gosset: Oxford University Press, USA, 1990.
[7] L.-H. Chen and H.-D. Hsiao, "Feature selection to diagnose a business crisis by using a real GA-based support vector machine: An empirical study," Expert Systems with Applications, vol. 35, pp. 1145-1155, 2008.
[8] Z. Hua, Y. Wang, X. Xu, B. Zhang, and L. Liang, "Predicting corporate financial distress based on integration of support vector machine and logistic regression," Expert Systems with Applications, vol. 33, pp. 434-440, 2007.
[9] K.-S. Shin, T. S. Lee, and H.-j. Kim, "An application of support vector machines in bankruptcy prediction model," Expert Systems with Applications, vol. 28, pp. 127-135, 2005.
[10] C.-H. Wu, G.-H. Tzeng, Y.-J. Goo, and W.-C. Fang, "A real-valued genetic algorithm to optimize the parameters of support vector machine for predicting bankruptcy," Expert systems with applications, vol. 32, pp. 397-408, 2007.
[11] D. West, "Neural network credit scoring models," Computers & Operations Research, vol. 27, pp. 1131-1152, 2000.
[12] L. Sun and P. P. Shenoy, "Using Bayesian networks for bankruptcy prediction: Some methodological issues," European Journal of Operational Research, vol. 180, pp. 738-753, 2007.
[13] H. Li, J. Sun, and J. Wu, "Predicting business failure using classification and regression tree: An empirical comparison with popular classical statistical methods and top classification mining methods," Expert Systems with Applications, vol. 37, pp. 5895-5904, 2010.
[14] K. Y. Tam and M. Y. Kiang, "Managerial applications of neural networks: the case of bank failure predictions," Management science, vol. 38, pp. 926-947, 1992.
[15] E. N. Ozkan-Gunay and M. Ozkan, "Prediction of bank failures in emerging financial markets: an ANN approach," Journal of Risk Finance, The, vol. 8, pp. 465-480, 2007.
[16] R. J. Bauer, Genetic algorithms and investment strategies: John Wiley & Sons, Inc., 1994.
[17] S.-W. Lin, K.-C. Ying, S.-C. Chen, and Z.-J. Lee, "Particle swarm optimization for parameter determination and feature selection of support vector machines," Expert Systems with Applications, vol. 35, pp. 1817-1824, 2008.
[18] F.-L. Chen and F.-C. Li, "Combination of feature selection approaches with SVM in credit scoring," Expert Systems with Applications, vol. 37, pp. 4902-4909, 2010.
[19] C.-F. Tsai, "Feature selection in bankruptcy prediction," Knowledge-Based Systems, vol. 22, pp. 120-127, 2009.
[20] X. Wu, V. Kumar, J. R. Quinlan, J. Ghosh, Q. Yang, H. Motoda, et al., "Top 10 algorithms in data mining," Knowledge and Information Systems, vol. 14, pp. 1-37, 2008.
[21] R. S. Sexton, R. S. Sriram, and H. Etheridge, "Improving decision effectiveness of artificial neural networks: a modified genetic algorithm approach," Decision Sciences, vol. 34, pp. 421-442, 2003.
[22] F. Lin, D. Liang, and E. Chen, "Financial ratio selection for business crisis prediction," Expert Systems with Applications, vol. 38, pp. 15094-15102, 2011.
[23] C.-L. Huang and C.-J. Wang, "A GA-based feature selection and parameters optimizationfor support vector machines," Expert Systems with applications, vol. 31, pp. 231-240, 2006.
[24] C.-F. Tsai and M.-L. Chen, "Credit rating by hybrid machine learning techniques," Applied Soft Computing, vol. 10, pp. 374-380, 2010.
[25] L. B. J. F. R. Olshen and C. J. Stone, "Classification and regression trees," Wadsworth International Group, 1984.
[26] G. R. Iversen and H. Norpoth, Analysis of variance: Sage, 1987.
[27] D. Wooff, "Logistic Regression: a Self‐learning Text," Journal of the Royal Statistical Society: Series A (Statistics in Society), vol. 167, pp. 192-194, 2004.
[28] W. R. Klecka, Discriminant analysis: Sage, 1980.
[29] A. L. Blum and P. Langley, "Selection of relevant features and examples in machine learning," Artificial intelligence, vol. 97, pp. 245-271, 1997.
[30] R. Kohavi and G. H. John, "Wrappers for feature subset selection," Artificial intelligence, vol. 97, pp. 273-324, 1997.
[31] I. Guyon and A. Elisseeff, "An introduction to variable and feature selection," The Journal of Machine Learning Research, vol. 3, pp. 1157-1182, 2003.
[32] H. Liu and L. Yu, "Toward integrating feature selection algorithms for classification and clustering," Knowledge and Data Engineering, IEEE Transactions on, vol. 17, pp. 491-502, 2005.
[33] J. Kittler, "Feature set search algorithms," Pattern recognition and signal processing, pp. 41-60, 1978.
[34] J. H. Holland, Adaptation in natural and artificial systems: An introductory analysis with applications to biology, control, and artificial intelligence: U Michigan Press, 1975.
[35] J. Kennedy, & Eberhart, R. C., " Particle swarm optimization," in Proceedings of IEEE Conference on Neural Network, 1995, pp. 1942-1948.
[36] S. Balcaen and H. Ooghe, "35 years of studies on business failure: an overview of the classic statistical methodologies and their related problems," The British Accounting Review, vol. 38, pp. 63-93, 2006.
[37] M. Srinivas and L. M. Patnaik, "Genetic algorithms: A survey," Computer, vol. 27, pp. 17-26, 1994.
[38] J. Kennedy and R. C. Eberhart, "A discrete binary version of the particle swarm algorithm," in Proceedings of IEEE International Conference on Systems, Man, and Cybernetics, 1997, pp. 4104-4108.
[39] R. A. Fisher, "The use of multiple measurements in taxonomic problems," Annals of eugenics, vol. 7, pp. 179-188, 1936.
[40] J. Neter, W. Wasserman, and M. H. Kutner, Applied linear statistical models vol. 4: Irwin Chicago, 1996.
[41] M. Bardos, "Detecting the risk of company failure at the Banque de France," Journal of Banking & Finance, vol. 22, pp. 1405-1419, 1998.
[42] V. S. Desai, J. N. Crook, and G. A. Overstreet Jr, "A comparison of neural networks and linear scoring models in the credit union environment," European Journal of Operational Research, vol. 95, pp. 24-37, 1996.
[43] R. A. Eisenbeis, "Problems in applying discriminant analysis in credit scoring models," Journal of Banking & Finance, vol. 2, pp. 205-219, 1978.
[44] P. Falbo, "Credit-scoring by enlarged discriminant models," Omega, vol. 19, pp. 275-289, 1991.
[45] B. J. Grablowsky, & Talley, W. K., "Probit and discriminant factors for classifying credit applicants: A comparison," Journal of Economics and Business, vol. 33, pp. 254-261, 1981.
[46] T. F. Martell and R. L. Fitts, "A quadratic discriminant analysis of bank credit card user characteristics," Journal of Economics and Business, vol. 33, pp. 153-159, 1981.
[47] G. A. OVERSTREET, E. L. BRADLEY, and R. S. KEMP, "The flat-maximum effect and generic linear scoring models: a test," IMA Journal of Management Mathematics, vol. 4, pp. 97-109, 1992.
[48] A. K. Reichert, C.-C. Cho, and G. M. Wagner, "An examination of the conceptual issues involved in developing credit-scoring models," Journal of Business & Economic Statistics, vol. 1, pp. 101-114, 1983.
[49] D. Titterington, "Discriminant analysis and related topics," Credit scoring and credit control, Oxford University Press, Oxford, pp. 53-73, 1992.
[50] R. A. Fisher and F. Yates, "Statistical tables for biological, agricultural and medical research," Statistical tables for biological, agricultural and medical research., 1949.
[51] D. N. Joanes, "Reject inference applied to logistic regression for credit scoring," IMA Journal of Management Mathematics, vol. 5, pp. 35-43, 1993.
[52] E. K. Laitinen and T. Laitinen, "Bankruptcy prediction: application of the Taylor's expansion in logistic regression," International Review of Financial Analysis, vol. 9, pp. 327-349, 2001.
[53] S. Westgaard and N. Van der Wijst, "Default probabilities in a corporate bank portfolio: A logistic model approach," European journal of operational research, vol. 135, pp. 338-349, 2001.
[54] J. C. Wiginton, "A note on the comparison of logit and discriminant models of consumer credit behavior," Journal of Financial and Quantitative Analysis, vol. 15, pp. 757-770, 1980.
[55] Y. Ding, X. Song, and Y. Zen, "Forecasting financial condition of Chinese listed companies based on support vector machine," Expert Systems with Applications, vol. 34, pp. 3081-3089, 2008.
[56] P.-W. Huang and C.-L. Liu, "Using genetic algorithms for feature selection in predicting financial distresses with support vector machines," in Proceedings of IEEE International Conference on Systems, Man and Cybernetics, 2006, pp. 4892-4897.
[57] Z. Lei-lei, H. Xiao-feng, and W. Lei, "Application of adaptive support vector machines method in credit scoring," in Proceedings of IEEE International Conference on Management Science and Engineering, 2009, pp. 1410-1415.
[58] P. M. Murphy, Aha, D. W. (2001). UCI Repository of machine learning databases. Available: http://archive.ics.uci.edu/ml/
[59] Taiwan Economic Journal. Available: http://www.tej.com.tw/twsite/
[60] G. H. John and P. Langley, "Estimating continuous distributions in Bayesian classifiers," in Proceedings of the Eleventh conference on Uncertainty in artificial intelligence, 1995, pp. 338-345.
[61] C.-I. Hsu, Y. L. Hsu, and P. L. Hsu, "Financial performance prediction using constraint-based evolutionary classification tree (CECT) approach," in Advances in Natural Computation, ed: Springer, 2005, pp. 812-821.
[62] P.-C. Ko and P.-C. Lin, "An evolution-based approach with modularized evaluations to forecast financial distress," Knowledge-Based Systems, vol. 19, pp. 84-91, 2006.
[63] Y. Liu, G. Wang, H. Chen, H. Dong, X. Zhu, and S. Wang, "An improved particle swarm optimization for feature selection," Journal of Bionic Engineering, vol. 8, pp. 191-200, 2011.
[64] W. Xu, S. Zhou, D. Duan, and Y. Chen, "A support vector machine based method for credit risk assessment," in Proceedings of IEEE International Conference on e-Business Engineering (ICEBE), 2010, pp. 50-55.
[65] C. W. Ou. (2012). Using genetic algorithm for feature selection in financial distress problem, National Center University, Computer Science and Information Engineering, Taiwan.
[66] M.-J. Kim and D.-K. Kang, "Ensemble with neural networks for bankruptcy prediction," Expert Systems with Applications, vol. 37, pp. 3373-3379, 2010.
[67] R. Maclin and D. Opitz, "Popular ensemble methods: An empirical study," Journal of Artificial Intelligence Research, vol. 11, pp. 169-198, 1999.
[68] T.-C. Tang and L.-C. Chi, "Neural networks analysis in business failure prediction of Chinese importers: A between-countries approach," Expert Systems with Applications, vol. 29, pp. 244-255, 2005.
[69] Financial Supervisory Commission. Available: http://www.fsc.gov.tw/ch/