Acknowledgement
본 논문은 2024 년도 산업통상자원부 및 한국산업기술진흥원의 산업혁신인재성장지원사업 (RS-2024-00415520)과 과학기술정보통신부 및 정보통신기획평가원의 ICT 혁신인재 4.0 사업의 연구결과로 수행되었음 (No. IITP-2022-RS-2022-00156310)
References
- Bin Sulaiman, Rejwan, Vitaly Schetinin, and Paul Sant, Review of machine learning approach on credit card fraud detection, Human-Centric Intelligent Systems 2.1, 55-68, 2022 https://doi.org/10.1007/s44230-022-00004-0
- 금융거래표(2009~), 국가정보포털(KOSIS), https://kosis.kr/search/search.do, 2024/4/12
- Benchaji, I., Douzi, S., El Ouahidi, B., Jaafari, J., Enhanced credit card fraud detection based on attention mechanism and LSTM deep model, J Big Data 8, 151, 2021
- Mohmad, Yanash Azwin., Credit Card Fraud Detection Using LSTM Algorithm, Wasit J. Comput. Math. Sci 1, 39-53, 2022 https://doi.org/10.31185/wjcm.70
- Dablain, Damien, Bartosz Krawczyk, and Nitesh V. Chawla, DeepSMOTE: Fusing deep learning and SMOTE for imbalanced data, IEEE Transactions on Neural Networks and Learning Systems, 2022
- Bhandari, H. N., Rimal, B., Pokhrel, N. R., Rimal, R., Dahal, K. R., & Khatri, R. K., Predicting stock market index using LSTM, Machine Learning with Applications, 9, 100320, 2022
- Abumohsen, Mobarak, Amani Yousef Owda, and Majdi Owda, Electrical load forecasting using LSTM, GRU, and RNN algorithms, Energies 16.5, 2283, 2023
- Credit Card Fraud Detection, Kaggle, 2021, https://www.kaggle.com/datasets/mlgulb/creditcardfraud/data, 2024/4/12
- Bertrand Lebichot, Gianmarco Paldino, Wissam Siblini, Liyun He, Frederic Oble, Gianluca Bontempi, Incremental learning strategies for credit cards fraud detection, International Journal of Data Science and Analytics, 12(2), 165-174, 2021 https://doi.org/10.1007/s41060-021-00258-0