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NTIS 바로가기지능정보연구 = Journal of intelligence and information systems, v.28 no.2, 2022년, pp.307 - 332
조수현 (이화여자대학교 빅데이터분석학) , 신경식 (이화여자대학교 경영대학)
One of the most intensively conducted research areas in business application study is a bankruptcy prediction model, a representative classification problem related to loan lending, investment decision making, and profitability to financial institutions. Many research demonstrated outstanding perfor...
Bank of Korea. (2020). Financial Statement Analysis for 2019. In Bank of Korea.
Belkoura, S., Zanin, M., & Latorre, A. (2019). Fostering interpretability of data mining models through data perturbation. Expert Systems With Applications, 137, 191-201. https://doi.org/10.1016/j.eswa.2019.07.001
Byrne, R. M. J. (2019). Counterfactuals in Explainable Artificial Intelligence (XAI): Evidence from Human Reasoning. International Joint Conference on Artificial Intelligence (IJCAI-19), 6276-6282.
Davidson, W. (2019). Financial Statement Analysis Basis For Management Advice. Association of International Certified Professional Accountants, Inc.
Fernandez, R. R., Diego, I. M. de, Acena, V., Fernandez-isabel, A., & Moguerza, J. M. (2020). Random forest explainability using counterfactual sets. Information Fusion, 63, 196-207. https://doi.org/10.1016/j.inffus.2020.07.001
Gadanecz, B., & Jayaram, K. (2008). Measures of financial stability - a review. IFC Conference on "Measuring Financial Innovation and Its Impact," 365-380.
Gedikli, F., Jannach, D., & Ge, M. (2014). How should i explain? A comparison of different explanation types for recommender systems. International Journal of Human Computer Studies, 72(4), 367-382. https://doi.org/10.1016/j.ijhcs.2013.12.007
Goyal, Y., Wu, Z., Ernst, J., Batra, D., Parikh, D., & Lee, S. (2019). Counterfactual visual explanations. 36th International Conference on Machine Learning, ICML 2019, 2019-June, 4254-4262.
Grath, R. M., Costabello, L., le Van, C., Sweeney, P., Kamiab, F., Shen, Z., & Lecue, F. (2018). Interpretable credit application predictions with counterfactual explanations. NIPS 2018 Workshop on Challenges and Opportunities for AI InFinancial Services: The Impact of Fairness, Explainability, Accuracy, and Privacy.
Guidotti, R., Monreale, A., Ruggieri, S., Giannotti, F., Pedreschi, D., & Turini, F. (2019). Factual and Counterfactual Explanations for Black Box Decision Making. IEEE Intelligent Systems, November/December, 14-23.
Guidotti, R., Monreale, A., Ruggieri, S., Pedreschi, D., Turini, F., & Giannotti, F. (2018). Local rule-based explanations of black box decision systems. In arXiv (Issue May).
Hashemi, M., & Fathi, A. (2020). PermuteAttack: Counterfactual explanation of machine learning credit scorecards. ArXiv.
Helfert, E. A. (2001). Financial Analysis Tools and Techniques: A Guide for Managers. McGrawHill Education.
Islam, M. R., Ahmed, M. U., Barua, S., & Begum, S. (2022). A Systematic Review of Explainable Artificial Intelligence in Terms of Different Application Domains and Tasks. Applied Sciences, 12(3). https://doi.org/10.3390/app12031353
Keane, M. T., & Smyth, B. (2020). Good Counterfactuals and Where to Find Them: A Case-Based Technique for Generating Counterfactuals for Explainable AI (XAI). Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 12311 LNAI, 163-178. https://doi.org/10.1007/978-3-030-58342-2_11
Kenny, E. M., Ford, C., Quinn, M., & Keane, M. T. (2021). explanations-by-example : The effect of explanations and error-rates in XAI user studies . Artificial Intelligence, 294, 103459. https://doi.org/10.1016/j.artint.2021.103459
Kenny, E. M., & Keane, M. T. (2021a). Explaining Deep Learning using examples: Optimal feature weighting methods for twin systems using post-hoc, explanation-by-example in XAI. Knowledge-Based Systems, 233, 107530. https://doi.org/10.1016/j.knosys.2021.107530
Kenny, E. M., & Keane, M. T. (2021b). On Generating Plausible Counterfactual and Semi-Factual Explanations for Deep Learning. The Thirty-Fifth AAAI Conference on Artificial Intelligence (AAAI-21), 11575-11585. http://arxiv.org/abs/2009.06399
Le, T., Wang, S., & Lee, D. (2020). GRACE : Generating Concise and Informative Contrastive Sample to Explain Neural Network Model ' s Prediction. KDD '20: Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 238-248. https://doi.org/https://doi.org/10.1145/3394486.3403066
Lundberg, S. M., & Lee, S. (2017). A Unified Approach to Interpreting Model Predictions. 31st Conference on Neural Information Processing Systems (NIPS 2017), Long Beach, CA, USA, Section 2, 1-10.
Mahajan, D., Tan, C., & Sharma, A. (2019). Preserving causal constraints in counterfactual explanations for machine learning classifiers. 33rd Conferenceon Neural Information Processing Systems.
Melanie, M. (1999). An Introduction to Genetic Algorithms. A Bradford Book The MIT Press.
Miller, T. (2019). Explanation in artificial intelligence : Insights from the social sciences. Artificial Intelligence, 267, 1-38. https://doi.org/10.1016/j.artint.2018.07.007
Poyiadzi, R., Sokol, K., Santos-rodriguez, R., Bie, T. de, & Flach, P. (2020). FACE : Feasible and Actionable Counterfactual Explanations. AAAI/ACM Conference on AI, Ethics, and Society (AIES). https://doi.org/https://doi.org/10.1145/3375627.3375850 1
Ribeiro, M. T., Singh, S., & Guestrin, C. (2018). Anchors : High-Precision Model-Agnostic Explanations. The Thirty-Second AAAI Conference on Artificial Intelligence (AAAI-18), 1527-1535.
Schneider, C. Q., & Rohlfing, I. (2016). Case Studies Nested in Fuzzy-set QCA on Sufficiency: Formalizing Case Selection and Causal Inference. Sociological Methods and Research, 45(3), 526-568. https://doi.org/10.1177/0049124114532446
Stepin, I., Alonso, J. M., & Catala, A. (2021). A Survey of Contrastive and Counterfactual Explanation Generation Methods for Explainable Artificial Intelligence. IEEE Access, 9. https://doi.org/10.1109/ACCESS.2021.3051315
Waa, J. van der, Nieuwburg, E., Cremers, A., & Neerincx, M. (2021). Evaluating XAI : A comparison of rule-based and example-based explanations. Artificial Intelligence, 291, 103404. https://doi.org/10.1016/j.artint.2020.103404
Wachter, S., Mittelstadt, B., & Russell, C. (2018). COUNTERFACTUAL EXPLANATIONS WITHOUT OPENING THE BLACK BOX : AUTOMATED DECISIONS AND THE GDPR. Harvard Journal of Law & Technology, 31(2), 842-887.
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