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一 、中文部分：
王進德（2015）編著，類神經網路與模糊控制理論入門與應用，全華圖書
股份有限公司。
王濟川與郭志剛（2003）著，Logistic迴歸模型-方法及應用，五南圖書出版
公司。
林萍珍（2008），投資分析－含MATLAB應用、類神經網路與遺傳演算法
模型，新陸書局。
林維垣（2000），人工智慧在投資策略之應用—電腦模擬與其積效之統計
分析，華泰文化事業公司。
周鵬程（2008）編著，類神經網路入門-活用Matlab，全華圖書。
郝沛毅、李御璽與黃嘉彥（2017） 編譯，資料採礦 DATA MINING Concepts
and Techniques 3/e，高立圖書有限公司。
郭憲與方勇純(2018)編著，深入淺出強化學習原理入門，電子工業出版社。
潘文超(2011) 編著，果蠅最佳化演算法，滄海書局。
潘文超(2019) 編著，資料採礦寶典:使用R語言。
謝邦昌與鄭宇庭（2016）編著，統計機器學習在R中實踐，新陸書局股份有
限公司。
鍾振蔚（2015）譯，R軟體資料分析基礎御應用，旗標出版股份有限公司。
韓偉與毛俊杰（2014）編著，R語言與商業智能，電子工業出版社。
薛薇（2016）編著，R語言數據挖掘方法與應用，電子工業出版社。
薛薇（2016）編著，R語言數據挖掘-Data Mining with R，中國人民大學出
版社。
蘇木春與張孝德（2012）編著，機器學習:類神經網路、模糊系統以及基因
演算法則，全華圖書股份有限公司。
二 、英文部份：
Addo, P.M., Guegan, D., and Hassani, B. (2018), “Credit Risk Analysis Using Machine and Deep Learning Models,”MDPI.
Aldrich, J. H., and Nelson, F. D. (1984), “Linear Probability, Logit and Probit models,” Sage Publications.
Bennett, K. P., and Campbell, C. (2000), “Support Vector Machines: Hype or Hallelujah ?,” SIGKDD Explorations, 2 (2): 1-13.
Bradley, A. P. (1997), “The use of the area under the ROC curve in the
evaluation of machine learning algorithms,”Pattern Recogn. 30: 1145–1159.
Breiman, L. (2001), “Random Forest,” Machine Learning, 45: 5-32.
Cortes, C., and Vapnik, V. (1995), “Support Vector Networks,” Machine Learning, 20: 273–297.
Crammer, K., and Singer, Y. (2002), “On the Learnability and Design of Output Codes for Multiclass Problems,” Machine Learning, 47: 201-233.
Doumpos, M., and Zopounidis, C. (2009),“Monotonic support vector machines for credit risk rating,” New Mathematics and Natural Computation, 5: 557-570.
Evers, G. I., and Ghalia, M. B. (2009), “Regrouping Particle Swarm Optimization: A New Global Optimization Algorithm with Improved Performance Consistency Across Benchmarks,” IEEE International Conference, 3901-3908.
Ertel , W. (2017),“Introduction to Artificial Intelligence,” Springer.
Ghatak, A. (2017),“Machine Learning with R,” Springer.
Gulli, A., and Pal, S. (2017), “Deep Learning with Keras,” Packt publishing, Birmingham.
Hand, D. J., and Till, R. J. (2001), “A simple generalization of the area under the ROC curve to multiple class classification problems,” Machine Learning, 45(2), 171–186.
Ho, T. K. (1995), “Random Decision Forests,” Proceedings of 3rd International Conference on Document Analysis and Recognition, 1: 278-282.
Hu, Y., Wang, J., and Yang, X.G.(2015), “A study on early-warning of bank customer loan defaults based on panel Logit model,” Systems Engineering - Theory & Practice, 35(7): 1752-1759.
Jagtiani, J., Kolari, J., Lemieux, C., and Shin, H. (2003), “Early warning models for bank supervision: Simpler could be better,” Federal Reserve Bank of Chicago, 49-60.
Kim H. S., and Sohn S. Y. (2010), “Support vector machines for default prediction of SMEs based on technology credit,”European J. Oper. Res. 201, 838-846.
Kureishy, A., Meley, C., and Ben, M. (2018),“Achieving Real Business Outcomes from Artificial Intelligence,” O'Reilly Media, Inc.
Lantz, B. (2015),“Machine Learning with R (2rd Edition),” Packt Publishing.
Liaw, A., and Wiener, M. (2002), “Classification and Regression by Random Forest,” R News,2 (3): 18-22.
Lin, W. Y. (2016), “A Novel 3D Fruit Fly Optimization Algorithm and its Applications in Economics,” Neural Computing and Applications, 27 (5): 1391–1413.
Lin, W. Y., Chu, Y. D., and Liao, D. Y. (2018), “Using Artificial Intelligence Technology for Corporate Financial Diagnostics,” Int. J. Bus. Financ. Manage. Res. BPJ, 6 (2): 8-21.
Lin, W. Y., and Chu, Y. D. (2020), “Application of artificial intelligence investment in capital markets: A case study of two constituent stocks of Dow Jones,” Int. J. Bus. Financ. Manage. Res. BPJ, 8 (3): 33-47.
Matre, A. (2019), “Machine Learning in Default Prediction,”NHH.
Mohammad,N., and Onni,A.N. (2015), “Credit Risk Grading Model and Loan Performance of Commercial Banks in Bangladesh,” IISTE. Vol 7, No 13.
Moradi, S., and Rafiei, F. M. (2019), “A dynamic credit risk assessment model with data mining techniques: evidence from Iranian banks,” Springer.
Muzcek, P. (2010), “Credit Risk Monitoring in the Czech Banking Sector: Early Warning Model,” LAP LAMBERT Academic Publishing.
Ohlson, J. A. (1980), “Financial ratios and the probabilistic prediction of bankruptcy,” J. Account. Res. 18: 109–131.
Pan, W. T., Leu, Y. H., Zhu, W., and Lin, W. Y. (2017), “A data mining
approach to the analysis of a catering lean service project,” Intelligent Automation & Soft Computing, 23(2).
Parsopoulos, K., and Vrahatis, M.N. (2002), “Recent Approaches to Global Optimization Problems through Particle Swarm Optimization,” Natural Computing 116 (2-3): 235–306.
Raffinot, T.(2018), “Asset Allocation, Economic Cycles and Machine Learning,” NNT : 2017PSLED067.
Russell, S. J., and Norvig, P. (2010),“Artificial Intelligence-A Modern Approach (3rd Edition),” Prentice Hall.
Scrucca, L. (2013), “GA: A Package for Genetic Algorithms in R,” Journal of Statistical Software, 53 (4): 1-37.
Segal, M. R. (2004), “Machine Learning Benchmarks and Random Forest Regression,” Division of Biostatistics, University of California, San Francisco, CA.
Sevim, C., Oztekin, A., Bali, O., Gumus, S., and Guresen, E. (2014), “Developing an early warning system to predict currency crises,” European Journal of Operational Research 237(3):1095–1104.
Terry, H. P., and Hulsing, J. (2016),“Artificial Intelligence (AI, Machine Learning and Data Fuel the Future of Productivity ,” The Goldman Sachs Group, Inc.
Theobald, O. (2017),“Machine Learning For Absolute Beginners ,”
Independently Publishing.
Yang, Z. R., Platt, M. B., and Platt, H. D. (1999), “Probabilistic neural networks in bankruptcy prediction,” J. Business Res. 44: 67–74.
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