참고문헌
- Bach, S., Binder, A., Montavon, G., Klauschen, F., Müller, K. R., and Samek, W., "On pixel-wise explanations for non-linear classifier decisions by layer-wise relevance propagation", PloS one, Vol. 10, No. 7, 2015, e0130140.
- Berry, M. and Linoff, "Data Mining Techniques: For Marketing, Sales, and Customer Support", Wiley,1997, ISBN: 0471179809, 9780471179801.
- Biecek, P., Chlebus, M., Gajda, J., Gosiewska, A., Kozak, A., Ogonowski, D. and Wojewnik, P., "Enabling Machine Learning Algorithms for Credit Scoring: Explainable Artificial Intelligence (XAI) methods for clear understanding complex predictive models", arXiv preprint arXiv:2104.06735, 2021.
- Blei, D. M., "Probabilistic topic models", Communications of the ACM, Vol. 55, No. 4, pp. 77-84, 2012. https://doi.org/10.1145/2133806.2133826
- Blei, D. M., Ng, A. Y., and Jordan, M. I., "Latent dirichlet allocation", The Journal of Machine Learning Research, Vol. 3, 2003, pp. 993-1022.
- Chen, B., "Deep Learning-Based Natural Language Processing Model for Explanable Personalization Recommendation Service", Kyunghee University, 2022.
- Chu, H., Shin, H., Choi, S., Yoo, Y., and Park, C., "Sensitivity Analysis Using Explainable AI for Building Energy Use", Journal of the Architectural Institute of Korea, Vol. 38, No. 11, 2022, pp. 279-287.
- Chun, Y., Kim, S., Lee, J., and Woo, J., "Study on credit rating model using explainable AI", Journal of the Korean Data And Information Science Society, Vol. 32, No. 2, 2021, pp. 283-295. https://doi.org/10.7465/jkdi.2021.32.2.283
- Deerwester, S., Dumais, S. T., Furnas, G. W., Landauer, T. K., and Harshman, R., "Indexing by latent semantic analysis", Journal of the American Society for Information Science, Vol. 41, No. 6, 1990, pp. 391-407. https://doi.org/10.1002/(SICI)1097-4571(199009)41:6<391::AID-ASI1>3.0.CO;2-9
- Guidotti, R., Monreale, A., Ruggieri, S., Turini, F., Giannotti, F., and Pedreschi, D., "A survey of methods for explaining black box models", ACM Computing Surveys (CSUR), Vol. 51, No. 5, 2018, pp. 1-42. https://doi.org/10.1145/3236009
- Han, J. and Choi, J., "Explainable Artificial Intelligence", Journal of KSNVE, Vol. 27, No. 6, pp. 2017, 8-13.
- Han, S. and Kim, T., "News Big Data Analysis of 'Metaverse' Using Topic Modeling Analysis", Journal of Digital Contents Society, Vol. 22, No. 7, 2021, pp. 1091-1099. https://doi.org/10.9728/dcs.2021.22.7.1091
- Hansen, D., Shneiderman, B., and Smith, M. A., "Analyzing social media networks with NodeXL: Insights from a connected world", Morgan Kaufmann, 2010.
- Hofmann, T., "Probabilistic latent semantic indexing", In Proceedings of the 22nd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, 1999, August, pp. 50-57.
- Hwang, H., "Sentiment Analysis based on Semi-Supervised Learning for using XAI", Seoul National University of Science and Technology, 2022.
- Hwang, S., "Knowledge graph-based UI for explainable recommendation", Yonsei University, 2021.
- Jaegal, D., "The Applicability of Social Network Analysis for Program Evaluation in Public Sector", Journal of Research Methodology, Vol.4, No.3, 2019, pp. 1-32. https://doi.org/10.21487/jrm.2019.11.4.3.1
- Jung, W., Yoon, J., and Kim, K., "Accounting Fraud Detection Using Forensic Techniques Based on Sentiment Analysis and Interpretable Machine Learning: Focused on Internal Control over Financial Reporting", Korean Accounting Review, Vol.46, No.6, 2021, pp. 181-218.
- Kang, K. and Park, S., "Keyword Analysis of KCI Journals on Business Administration using Web Crawling and Machine Learning", Korean Journal of Business Administration, Vol.32, No.4, 2019, pp. 597-615. https://doi.org/10.18032/kaaba.2019.32.4.597
- Kim, B., Jeong, M., Jeon, S., and Shin, D., "Global research trends on geospatial information by keyword network analysis", Journal of Korea Spatial Information Society, Vol.23, No.1, 2015, pp. 69-77 https://doi.org/10.12672/ksis.2015.23.1.069
- Kim, H. and Shin, S., "A Study on Explanable Artificial Intelligence (XAI)- based Machine Learning Feature Screening for Malicious Web Site Detection", Proceedings of Symposium of the Korean Institute of Communications and Information Sciences, 2020, pp. 411-412.
- Kim, M., Choi, J., and Kim, H., "The Framework of Research Network and Performance Evaluation on Personal Information Security: Social Network Analysis Perspective", Journal of Intelligence and Information System, Vol. 20, No. 1, 2014, pp. 177-193. https://doi.org/10.13088/jiis.2014.20.1.177
- Kim, M. and Hong, T., " Exploratory application of explainable artificial intelligence techniques to secure the reliability of deep learning-based personal credit evaluation model", Proceedings of the Korea Intelligent Information System Society Conference, 2022, pp. 194-194.
- Kim, S., Kim, D., and Kang, J., "A method suggesting the reason for the user recommendation: Design Sci Methodology methodology", Proceedings of Journal of Korea Service Management Society, 2022, pp. 130-130.
- Kim, S., Kim, W., Jang, Y., and Kim, H., "Development of Explainable AI-Based Learning Support System", The Journal of Korean Association of Computer Education, Vol. 24, No. 1, 2021, pp. 107-115.
- Kim, S. and Lee, G., "A Study on the Explainability and Hyperparameter Characteristics of Deep Neural Networks: A Case Study of SMEs Credit Scoring System", Journal of SME Finance, Vol.42, No.1, 2022, pp. 3-37. https://doi.org/10.33219/JSMEF.2022.42.1.001
- Kim, T., "COVID-19 News Analysis Using News Big Data: Focusing on Topic Modeling Analysis", The Journal of the Korea Contents Association, Vol. 20, No. 5, 2020, pp. 457-466.
- Lee, J., Park, J., Yoon, J., Lee, S., Yeom, S., Lee, Y., and Yoon, H., "Analyzing Precedents for Sports Match Fixing by Applying Topic Modeling", The Korean Journal of Measurement and Evaluation in Physical Education and Sports Science, Vol. 23, No. 2, 2021, pp. 51-65.
- Lee, S., Son, C., Shin, S., and Lee, K., "Airfoil Inverse Design using XAI(eXplainable Artificial Intelligence)", The Korean Society for Aeronautical and Space Sciences, 2021, pp. 50-51.
- Lundberg, S. and Lee, S., "A unified approach to interpreting model predictions. In Advances in Neural Information Processing Systems", 2017, pp. 4765-4774.
- Mohseni, S., Zarei, N., & Ragan, E. D. "A multidisciplinary survey and framework for design and evaluation of explainable AI systems", ACM Transactions on Interactive Intelligent Systems (TiiS), 11(3-4),2021, 1-45. https://doi.org/10.1145/3387166
- Moon, Y., "Analysis of Trends in Havruta Research based on Topic Modeling", The Journal of Learner-Centered Curriculum and Instruction, Vol. 20, No. 4, 2020, pp. 1149-1175. https://doi.org/10.22251/jlcci.2020.20.4.1149
- Oh, H., Son, A., and Lee, Z., "Occupational accident prediction modeling and analysis using SHAP", Journal of Digital Contents Society, Vol. 22, No. 7, 2021, pp. 1115-1123. https://doi.org/10.9728/dcs.2021.22.7.1115
- Park, J. and Kwak, K., "The Effect of Patent Citation Relationship on Business Performance : A Social Network Analysis Perspective", Journal of Intelligence and Information Systems, Vol. 19, No. 3, 2013, pp. 127-139. https://doi.org/10.13088/jiis.2013.19.3.127
- Park, S., Jeon, J., Kim, S., and Kim, S., "A Big-Data Analysis of Presidential Issue Ownership of two Prestigious Korean Newspapers: Focusing on LDA (latent Dirichlet allocation) Topic Modeling", The Journal of Political Science & Communication, Vol. 20, No. 3, 2017, pp. 25-55. https://doi.org/10.15617/psc.2017.10.31.3.25
- Ribeiro, M. T., Singh, S., and Guestrin, C., "Why should I trust you?" Explaining the predictions of any classifier", In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2016, pp. pp. 1135-1144.
- Shin, K., Lee, Y., Bae, B., Lee, S., Hong, H., Choi, Y., and Lee, S., "Trustworthy AI Framework for Malware Response", Journal of the Korea Institute of Information Security & Cryptology, Vol. 32, No. 5, 2022, pp. 1019-1034.
- Shortliffe, Edward H., and Bruce, G., "A Model of inexact reasoning in medicine", Mathematical Biosciences, Vol.23, No.3-4, 1975, pp. 351-379. https://doi.org/10.1016/0025-5564(75)90047-4
- Son, J., Woo, S., Paek, H., Hwang, B., and Choi, S., "False positive reduction in anomaly detection using XAI", Proceedings of the Korean Information Science Society Conference, 2022, pp. 609-611.
- Van Lent, M., Fisher, W., and Mancuso, M. "An explainable artificial intelligence system for small-unit tactical behavior", IAAI'04: Proc. 16th Conf. on Innovative Applications of Artificial Intelligence (p./pp. 900-907), San Jose, CA, USA: AAAI Press, ISBN: 978-0-262-51183-4, 2004.