參考文獻 |
Altman, E. I. (1968). Financial ratios, discriminant analysis and the prediction of corporate bankruptcy. The Journal of Finance, 23(4), 589-609.
Altman, E. I., Haldeman, R. G., & Narayanan, P. (1977). ZETATM analysis A new model to identify bankruptcy risk of corporations. Journal of banking & finance, 1(1), 29-54.
Arora, P., & Varshney, S. (2016). Analysis of K-Means and K-Medoids algorithm for big data. Procedia Computer Science, 78, 507-512.
Beaver, W. H. (1966). Financial ratios as predictors of failure. Journal of accounting research, 71-111.
Beaver, W. H. (1968). Market prices, financial ratios, and the prediction of failure. Journal of accounting research, 179-192.
Berry, M. J., & Linoff, G. (1997). Data mining techniques: for marketing, sales, and customer support: John Wiley & Sons, Inc.
Chen, K. H., & Shimerda, T. A. (1981). An empirical analysis of useful financial ratios. Financial Management, 51-60.
Chen, W.-S., & Du, Y.-K. (2009). Using neural networks and data mining techniques for the financial distress prediction model. Expert Systems with Applications, 36(2), 4075-4086.
Chou, C.-H., Hsieh, S.-C., & Qiu, C.-J. (2017). Hybrid genetic algorithm and fuzzy clustering for bankruptcy prediction. Applied soft computing, 56, 298-316.
Cortes, C., & Vapnik, V. (1995). Support-vector networks. Machine Learning, 20(3), 273-297. doi:10.1007/bf00994018
Dueck, D., & Frey, B. J. (2007). Non-metric affinity propagation for unsupervised image categorization. Paper presented at the Computer Vision, 2007. ICCV 2007. IEEE 11th International Conference on.
Frey, B. J., & Dueck, D. (2007). Clustering by passing messages between data points. science, 315(5814), 972-976.
Furey, T. S., Cristianini, N., Duffy, N., Bednarski, D. W., Schummer, M., & Haussler, D. (2000). Support vector machine classification and validation of cancer tissue samples using microarray expression data. Bioinformatics, 16(10), 906-914.
Ghodousi, M., Alesheikh, A. A., & Saeidian, B. (2016). Analyzing public participant data to evaluate citizen satisfaction and to prioritize their needs via K-means, FCM and ICA. Cities, 55, 70-81.
Ghodselahi, A. (2011). A hybrid support vector machine ensemble model for credit scoring. International Journal of Computer Applications, 17(5), 1-5.
Grupe, F. H., & Mehdi Owrang, M. (1995). Data base mining discovering new knowledge and competitive advantage. Information System Management, 12(4), 26-31.
Hsieh, N.-C. (2005). Hybrid mining approach in the design of credit scoring models. Expert Systems with Applications, 28(4), 655-665.
Hsu, C.-W., Chang, C.-C., & Lin, C.-J. (2003). A practical guide to support vector classification.
Lenard, M. J., Madey, G. R., & Alam, P. (1998). The design and validation of a hybrid information system for the auditor’s going concern decision. Journal of Management Information Systems, 14(4), 219-237.
Lensberg, T., Eilifsen, A., & McKee, T. E. (2006). Bankruptcy theory development and classification via genetic programming. European journal of operational research, 169(2), 677-697.
Lin, W.-Y., Hu, Y.-H., & Tsai, C.-F. (2012). Machine learning in financial crisis prediction: a survey. IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), 42(4), 421-436.
Liu, X.-Z., & Feng, G.-C. (2008). Kernel bisecting k-means clustering for SVM training sample reduction. Paper presented at the Pattern Recognition, 2008. ICPR 2008. 19th International Conference on.
MacQueen, J. (1967). Some methods for classification and analysis of multivariate observations. Paper presented at the Proceedings of the fifth Berkeley symposium on mathematical statistics and probability.
Mantovani, R. G., Rossi, A. L., Vanschoren, J., Bischl, B., & Carvalho, A. C. (2015). To tune or not to tune: recommending when to adjust SVM hyper-parameters via meta-learning. Paper presented at the Neural Networks (IJCNN), 2015 International Joint Conference on.
McCarty, J. A., & Hastak, M. (2007). Segmentation approaches in data-mining: A comparison of RFM, CHAID, and logistic regression. Journal of business research, 60(6), 656-662.
Min, J. H., & Lee, Y.-C. (2005). Bankruptcy prediction using support vector machine with optimal choice of kernel function parameters. Expert Systems with Applications, 28(4), 603-614. doi:https://doi.org/10.1016/j.eswa.2004.12.008
Ohlson, J. A. (1980). Financial ratios and the probabilistic prediction of bankruptcy. Journal of accounting research, 109-131.
Shin, K.-S., Lee, T. S., & Kim, H.-j. (2005). An application of support vector machines in bankruptcy prediction model. Expert Systems with Applications, 28(1), 127-135.
Telmoudi, F., El Ghourabi, M., & Limam, M. (2011). RST–GCBR‐Clustering‐Based RGA–SVM Model for Corporate Failure Prediction. Intelligent Systems in Accounting, Finance and Management, 18(2-3), 105-120.
Tsai, C.-F. (2014). Combining cluster analysis with classifier ensembles to predict financial distress. Information Fusion, 16, 46-58.
Tsai, C.-F., & Chen, M.-L. (2010). Credit rating by hybrid machine learning techniques. Applied soft computing, 10(2), 374-380.
Tsai, C.-F., Hu, Y.-H., Hung, C.-S., & Hsu, Y.-F. (2013). A comparative study of hybrid machine learning techniques for customer lifetime value prediction. Kybernetes, 42(3), 357-370.
Tsai, C.-F., & Hung, C. (2014). Modeling credit scoring using neural network ensembles. Kybernetes, 43(7), 1114-1123.
West, D., Dellana, S., & Qian, J. (2005). Neural network ensemble strategies for financial decision applications. Computers & operations research, 32(10), 2543-2559.
Xu, X., & Wang, Y. (2009). Financial failure prediction using efficiency as a predictor. Expert Systems with Applications, 36(1), 366-373.
Yeh, C.-C., Chi, D.-J., & Hsu, M.-F. (2010). A hybrid approach of DEA, rough set and support vector machines for business failure prediction. Expert Systems with Applications, 37(2), 1535-1541.
Žalik, K. R. (2008). An efficient k′-means clustering algorithm. Pattern recognition letters, 29(9), 1385-1391.
Zhang, G., Hu, M. Y., Patuwo, B. E., & Indro, D. C. (1999). Artificial neural networks in bankruptcy prediction: General framework and cross-validation analysis. European journal of operational research, 116(1), 16-32.
Zou, G. (2004). A modified poisson regression approach to prospective studies with binary data. American journal of epidemiology, 159(7), 702-706. |