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Contents 1
Experimental analysis of bankruptcy prediction with SHAP framework on polish companies / Tuguldur Enkhtuya ; Dae-Ki Kang 1
Abstract 1
1. Introduction 1
2. Background 2
3. Methodology 3
3.1. Algorithms 3
3.2. Data 3
4. Experiment and Results 4
5. Conclusion 5
References 5
번호 | 참고문헌 | 국회도서관 소장유무 |
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1 | S.R. Islam, W. Eberle, S. Bundy and S.K. Ghafoor, “Infusing Domain Knowledge in AI-based "Black Box" Models for Better Explainability with Application in Bankruptcy Prediction,” in Proc. ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2019, Anomaly Detection in Finance Workshop, Anchorage AK USA Aug. 4-8, 2019. DOI: https://doi.org/10.48550/arXiv.1905.11474 | 미소장 |
2 | C. Molnar, Interpretable Machine Learning: A Guide for Making Black Box Models Explainable (2nd ed.), Independently published, 2022. | 미소장 |
3 | A. Roth, The Shapley Value, Cambridge University Press, 1988. | 미소장 |
4 | D. Unzueta, “Fully Connected Layer vs. Convolutional Layer: Explained.” Oct. 18, 2022 . builtin.com/machine-learning/fully-connected-layer, Accessed Feb. 17, 2023. | 미소장 |
5 | O. Biran and C. Cotton, “Explanation and Justification in Machine Learning: A Survey,” in Proc. IJCAI Workshop on Explainable Artificial Intelligence (XAI), Aug. 13, 2017. | 미소장 |
6 | J.L. Bellovary, S. Giacomino, and M.D. Akers, “A Review of Bankruptcy Prediction Studies: 1930-Present,”Journal of Financial Education, Vol. 33, pp. 1-42, January 2007. | 미소장 |
7 | E. Sfakianakis, “Bankruptcy Prediction Model for Listed Companies in Greece,” Investment Management and Financial Innovations, Vol. 18, No. 2, pp. 166-180, May 2021. DOI: http://dx.doi.org/10.21511/imfi.18(2).2021.14 | 미소장 |
8 | S. Tian, Y. Yu, and H. Guo, “Variable Selection and Corporate Bankruptcy Forecasts,” Journal of Banking &Finance, Vol. 52, pp. 89-100, March 2015. DOI: https://doi.org/10.1016/j.jbankfin.2014.12.003 | 미소장 |
9 | L. Cultrera, and X. Brédart, “Bankruptcy Prediction: The Case of Belgian SMEs,” Review of Accounting and Finance, Vol. 15, No. 1, pp. 101-119. February 2016. DOI: https://doi.org/10.1108/RAF-06-2014-0059 | 미소장 |
10 | B. Ramsundar, and R.B. Zadeh, “TensorFlow for Deep Learning,” Chapter 4. Fully Connected Deep Networks, March 2018. | 미소장 |
11 | L.F.S. Scabini, and O.M. Bruno, “Structure and Performance of Fully Connected Neural Networks: Emerging Complex Network Properties,” arXiv:2107.14062v1, July 2021. DOI: https://doi.org/10.48550/arXiv.2107.14062 | 미소장 |
12 | B. Rozemberczki, L.Watson, P.Bayer, H.T.Yang, Olivér Kiss, S. Nilsson and R. Sarkar “The Shapley Value in Machine Learning,” in Proc. 31st International Joint Conference on Artificial Intelligence (IJCAI-22), International Joint Conferences on Artificial Intelligence Organization, pp. 5572-5579. Feb 11, 2022. DOI:https://doi.org/10.48550/arXiv.2202.05594 | 미소장 |
13 | S.M. Lundberg, and S.-I. Lee, “A Unified Approach to Interpreting Model Predictions,” in Proc. 31st International Conference on Neural Information Processing Systems, pp. 4768–4777, December 2017. DOI:https://doi.org/10.48550/arXiv.1705.07874 | 미소장 |
14 | N.V. Chawla, K.W. Bowyer, L.O. Hall, and W. P. Kegelmeyer, “SMOTE: Synthetic Minority Over-sampling Technique,” Vol. 16, No. 1, pp. 321-357, June 2002. DOI: https://doi.org/10.1613/jair.953 | 미소장 |
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