以機器學習技術建立電子商務購物網站信用卡偽冒交易之風險預測 - 以台灣B2C電商購物網站為例

以作者查詢圖書館館藏

、以作者查詢臺灣博碩士

、以作者查詢全國書目

、勘誤回報

、線上人數：17

、訪客IP：18.119.248.54

姓名

胡玉健(Yu-Chien Hu) 查詢紙本館藏

畢業系所

資訊管理學系

論文名稱

以機器學習技術建立電子商務購物網站信用卡偽冒交易之風險預測 - 以台灣B2C電商購物網站為例
(Machine Learning Techniques for Credit Card Fraud Detection in E-Commerce Platforms: A Case Study on a Taiwan B2C Shopping Platform)

相關論文

★ 利用資料探勘技術建立商用複合機銷售預測模型	★ 應用資料探勘技術於資源配置預測之研究-以某電腦代工支援單位為例
★ 資料探勘技術應用於航空業航班延誤分析-以C公司為例	★ 全球供應鏈下新產品的安全控管-以C公司為例
★ 資料探勘應用於半導體雷射產業-以A公司為例	★ 應用資料探勘技術於空運出口貨物存倉時間預測-以A公司為例
★ 使用資料探勘分類技術優化YouBike運補作業	★ 特徵屬性篩選對於不同資料類型之影響
★ 資料探勘應用於B2B網路型態之企業官網研究-以T公司為例	★ 衍生性金融商品之客戶投資分析與建議-整合分群與關聯法則技術
★ 應用卷積式神經網路建立肝臟超音波影像輔助判別模型	★ 基於卷積神經網路之身分識別系統
★ 能源管理系統電能補值方法誤差率比較分析	★ 企業員工情感分析與管理系統之研發
★ 資料淨化於類別不平衡問題: 機器學習觀點	★ 資料探勘技術應用於旅客自助報到之分析—以C航空公司為例

檔案

[Endnote RIS 格式]

[Bibtex 格式]

[相關文章]

[文章引用]

[完整記錄]

[館藏目錄]

至系統瀏覽論文 (2030-1-1以後開放)

摘要(中)

隨著電子商務的蓬勃發展，信用卡支付已成為線上交易中不可或缺的重要環節。然而，相伴而生的信用卡欺詐行為亦日趨猖獗；對消費者權益、商家營收以及金融體系造成了嚴重衝擊。由於欺詐手法層出不窮、變化多端，傳統基於規則的風險驗證機制已很難全面識別並防範這些新型欺詐威脅。有鑑於此，構建高效精準的信用卡欺詐檢測模型以維護電商交易安全迫在眉睫。
本研究以台灣B2C電子商務購物平台網站為研究個案，目的是透過機器學習技術完善既有的RBRA(Rule-Based Risk Authentication)信用卡偽冒防欺詐框架。本研究收集並預處理了個案研究B2C電子商務購物平台網站2022年的信用卡交易記錄。並分別基於決策樹、隨機森林、邏輯回歸等多種機器學習算法訓練分類模型，以預測每一筆交易的潛在欺詐風險程度。且鑑於本個案研究樣本中信用卡偽冒詐欺類別資料不平衡的特性，本研究採用了SMOTE技術進行資料集增強，引入F1-Score、ROC曲線等指標作為模型評估的主要依據。並由混淆矩陣分析結果進一步印證實驗模型的分類精準度。此外，本研究也探討了信用卡偽冒欺詐交易時間熱點、高風險商品類別以及收件人地區等維度的特徵資料選取。
在理論與實踐相結合的基礎上，本研究提出將優化後的機器學習模型整合電子商務購物平台網站，實現每一筆信用卡交易的即時風險評估。對於高風險訂單，結帳流程將導向到發卡行執行3DS身份驗證，從而形成前後台協同的防欺詐架構，有效維護交易安全性、提升消費者使用體驗。總結來看，本研究為電子商務購物平台網站防範金融欺詐活動提供了技術路徑，對於維護電子商務健康發展和消費者權益具有重要意義。

關鍵字：#機器學習 #資料探勘 #信用卡詐欺偵測 #風險預測 #RBRA

摘要(英)

The surge in credit card fraud accompanying e-commerce growth poses severe threats, necessitating efficient fraud detection models to safeguard transaction security. This study employed a Taiwanese e-commerce platform to enhance the existing Rule-Based Risk Authentication (RBRA) framework using machine learning techniques.

We collected and preprocessed 2022 transaction data, including confirmed fraud cases, and trained classification models using algorithms like decision trees, random forests, and logistic regression. Addressing class imbalance with oversampling, experimental results demonstrated the random forest model′s superior performance, achieving an AUC of 0.976 and F1-score of 0.916, corroborated by exceptional classification accuracy. Feature analysis provided insights into fraud hotspots, high-risk product categories, and recipients′ geographic patterns, informing targeted prevention strategies. Integrating the optimized model enables real-time risk assessment, redirecting high-risk orders for 3D Secure verification while maintaining security and user experience. This study offers a technical pathway for e-commerce platforms to combat fraud, contributing to sustainable development and consumer protection. Leveraging machine learning optimizes fraud detection, fostering a secure online shopping environment.

Future work focuses on incorporating diverse data sources, exploring ensemble models, and enhancing maintainability to further improve real-time performance, accuracy, and robustness, cultivating an efficient and secure e-commerce ecosystem.
Keywords: #Machine Learning #Data Mining #Credit Card Fraud Detection #Risk Prediction #RBRA

關鍵字(中)

★ 機器學習
★ 資料探勘
★ 信用卡詐欺偵測
★ 風險預測

關鍵字(英)

★ Machine Learning
★ Data Mining
★ Credit Card Fraud Detection
★ Risk Prediction
★ RBRA

論文目次

誌謝 iv
摘要 v
Abstract vi
目錄 vii
圖目錄 x
表目錄 xii
第一章緒論 1
1. 研究背景與動機 1
1.1 背景 1
1.2 動機 8
1.3 研究範圍與目的 16
1.4 研究貢獻 19
第二章文獻探討 21
2.1 信用卡偽冒詐欺 21
2.2 本研究所使用的模型探討 30
第三章研究方法 33
3.1. 研究設計 33
3.2 研究架構 35
3.3 研究對象 36
3.3.1 個案公司 36
3.3.2 個案研究樣本 37
3.3.3 資料前處理步驟 41
3.4 實驗環境說明 42
3.4.1 硬體環境 42
3.4.2 軟體環境 42
3.5 研究個案資料集解析 43
3.5.1資料預處理： 43
3.5.2特徵工程： 43
3.5.3資料不平衡處理: 43
3.6 實驗評估方法 51
第四章研究結果與分析 52
4.1 實驗結果說明 52
4.2結果分析與討論 59
4.2.1 交易時間： 59
4.2.2 交易地理區域： 60
4.3.3 信用卡偽冒詐欺的商品類型： 61
第五章研究結論 62
5.1 研究貢獻 63
5.2 研究限制 64
5.3 未來研究方向 65
參考文獻 66
1. 中文文獻 66
2. 英文文獻 67

圖目錄
圖1：個案研究公司Rule-based 手動黑名單管理時序圖 6
圖2：個案研究公司 Rule-based 商品類管理UI截圖 7
圖3：個案研究公司2016至2022年信用卡詐欺率 12
圖4：個案研究公司2016至2022年信用卡詐欺呆帳金額 13
圖5：個案研究公司2016至2022年信用卡詐欺交易攔單 13
圖6：個案研究公司RBRA系統架構圖 20
圖7：本研究的流程規劃及設計 34
圖8：研究架構圖 35
圖9：詐欺與非詐欺資料不平衡 44
圖10：在Orange中引入Python Script實作SMOTE範例 45
圖11：Orange中引入Python Script進行SMOTE處理 46
圖12：資料前處理流程圖 47
圖13：個案研究公司RBRA整合購物網站流程 49
圖14：RBRA Assess API轉導交易3D授權驗證流程 50
圖15：ROC曲線圖 53
圖16：各分類器的混淆矩陣 58
圖17：信用卡偽冒詐欺時間交易分佈 59
圖18：信用卡偽冒詐欺收件人地理區域分佈 60
圖19：信用卡偽冒詐欺商品類型 61

表目錄
表1：2020 至 2022 年信用卡詐欺型態統計比例表 14
表2：信用卡詐欺類型備註說明: 14
表3：總結了近期信用卡異常檢測研究的限制 28
表4：總結了近期信用卡異常檢測研究的限制(續) 29
表5：來源資料Attribute說明 37
表6：去識別化的欄位 39
表7：個案研究資料集分類說明 40
表8：混淆矩陣 - Confusion Matrix 51
表9：No sampling, test on training data 54
表10：Stratified Shuffle split, 10 random samples with 80% data 55
表11：Stratified 10-fold Cross validation and SMOTE imbalanced 56

參考文獻

1. 中文文獻
NCCC財團法人聯合信用卡處理中心NCCC (National Credit Card Center of R.O.C)公告 (2023.04.12). "疫情趨緩，景氣回溫，消費增加、信用卡詐欺也在增加." Retrieved 11.03, 2023,

Nilsonreport (2022). "Card Fraud Losses Reach $28.65 Billion."

VISAGroup (2023). "VISA secure-online-shopping." VISA Group. 2023,

YahooNews (2015). "反網路詐欺銀行局出手." 2023,
NCCC公開文件 (2022.11.16). "聯卡中心規畫提供更優質的詐欺偵測服務."

工商時報. (2023.07.25). 兆豐銀阻截盜刷升級第一線守住卡友額度高達96％. 黃有容. https://www.ctee.com.tw/news/20230725700986-439803
中華民國銀行公會. (2017.10.12). 辦理信用卡業務機構防制洗錢及打擊資恐注意事項範本. 中華民國銀行公會.
法務部調查局. (2017). 信用卡業務機構評估洗錢及資恐風險及訂定相關防制計畫指引法務部調查局.
金融監督管理委員會. (2022.12). 金融資安行動方案2.0. 金融監督管理委員會.
政大數據分析社, N. D. A. (2023.04.26). 【Machine Learning實作】以信用卡盜刷與建立推薦引擎來看機器學習. medium. https://medium.com/政大數據分析社-nccu-data-analytics/machine-learning實作-以信用卡盜刷與建立推薦引擎來看機器學習-cbb6a7823bbd
財團法人聯合信用卡處理中心身份驗證平台. (2023.01). 信用卡輔助持卡人身分驗證平臺. 財團法人聯合信用卡處理中心. https://www.nccc.com.tw/wps/wcm/connect/zh/home/BusinessOperations/CardBusiness/CardVerificationPlatform
梁遠見, 陳., 譚靖學,胡玉健. (2023). RBRA : Risk Assessment for Online Credit Card Transactions. Yahoo TechPulse.
陳淑玲律師. (2003.05.02). 信用卡民事實務見解之研究. 陳淑玲律師.

2. 英文文獻
Afriyie, J. K., Tawiah, K., Pels, W. A., Addai-Henne, S., Dwamena, H. A., Owiredu, E. O., Ayeh, S. A., & Eshun, J. (2023). https://doi.org/10.1016/j.dajour.2023.100163

Ahmed, M., Mahmood, A. N., & Islam, M. R. (2016). A survey of anomaly detection techniques in financial domain. Future Generation Computer Systems, 55, 278-288.

Akkoç, S. (2012). An empirical comparison of conventional techniques, neural networks and the three stage hybrid Adaptive Neuro Fuzzy Inference System (ANFIS) model for credit scoring analysis: The case of Turkish credit card data. European Journal of Operational Research, 222, 168–178. https://doi.org/10.1016/j.ejor.2012.04.009

Alfaiz, N. S., & Fati, S. M. (2022). Enhanced Credit Card Fraud Detection Model Using Machine Learning. Electronics, 11(4).

Bhattacharyya, S., Jha, S., Tharakunnel, K., & Westland, J. C. (2011). Data mining for credit card fraud: A comparative study. Decision Support Systems, 50(3), 602-613. https://doi.org/https://doi.org/10.1016/j.dss.2010.08.008

Brain, S. (2013). Credit card fraud statistics. Retrieved from.
Carcillo, F., Le Borgne, Y.-A., Caelen, O., Kessaci, Y., Oblé, F., & Bontempi, G. (2021). Combining unsupervised and supervised learning in credit card fraud detection. Information Sciences, 557, 317-331. https://doi.org/https://doi.org/10.1016/j.ins.2019.05.042

Carminati, M., Caron, R., Maggi, F., Epifani, I., & Zanero, S. (2015). BankSealer: A decision support system for online banking fraud analysis and investigation. computers & security, 53, 175-186.

Chawla, N. V., Bowyer, K. W., Hall, L. O., & Kegelmeyer, W. P. (2002). SMOTE: synthetic minority over-sampling technique. Journal Of Artificial Intelligence Research, 16, 321-357.

Chen, J., Tao, Y., Wang, H., & Chen, T. (2015). Big data based fraud risk management at Alibaba. The Journal of Finance and Data Science, 1(1), 1-10.

Cherkassky, V., & Ma, Y. (2004). Practical selection of SVM parameters and noise estimation for SVM regression. Neural Networks, 17(1), 113-126. https://doi.org/https://doi.org/10.1016/S0893-6080(03)00169-2

Crook, J. N., Edelman, D. B., & Thomas, L. C. (2007). Recent developments in consumer credit risk assessment. European Journal of Operational Research, 183(3), 1447-1465. https://doi.org/https://doi.org/10.1016/j.ejor.2006.09.100

Cybersixgill. (2023). STATE OF THE CYBERCRIME UNDERGROUND. Cybersixgill.
Dal Pozzolo, A., Caelen, O., Johnson, R. A., & Bontempi, G. (2015). Calibrating probability with undersampling for unbalanced classification. 2015 IEEE symposium series on computational intelligence,

Deepanath, C., & Prasad, K. (2019). IEEE-CIS fraud detection.

Dong, Q., Gong, S., & Zhu, X. (2017). Class rectification hard mining for imbalanced deep learning. Proceedings of the IEEE international conference on computer vision,

Forough, J., & Momtazi, S. (2021). Ensemble of deep sequential models for credit card fraud detection. Applied Soft Computing, 99, 106883. https://doi.org/https://doi.org/10.1016/j.asoc.2020.106883

Friedman, J. H. (1999.02.24). Greedy Function Approximation A Gradient Boosting Machine. https://jerryfriedman.su.domains/ftp/trebst.pdf

Fu, K., Cheng, D., Tu, Y., & Zhang, L. (2016, 2016//). Credit Card Fraud Detection Using Convolutional Neural Networks. Neural Information Processing, Cham.

Gao, J., Gong, L., Wang, J. Y., & Mo, Z. C. (2018, 16-19 Dec. 2018). Study on Unbalanced Binary Classification with Unknown Misclassification Costs. 2018 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM),

Islam, M. A., Uddin, M. A., Aryal, S., & Stea, G. (2023). An ensemble learning approach for anomaly detection in credit card data with imbalanced and overlapped classes. Journal of Information Security and Applications, 78. https://doi.org/10.1016/j.jisa.2023.103618

Itoo, F., Meenakshi, & Singh, S. (2021). Comparison and analysis of logistic regression, Naïve Bayes and KNN machine learning algorithms for credit card fraud detection. International Journal of Information Technology, 13(4), 1503-1511. https://doi.org/10.1007/s41870-020-00430-y

Jurgovsky, J., Granitzer, M., Ziegler, K., Calabretto, S., Portier, P.-E., He-Guelton, L., & Caelen, O. (2018). Sequence classification for credit-card fraud detection. Expert Systems with Applications, 100, 234-245. https://doi.org/https://doi.org/10.1016/j.eswa.2018.01.037

Karthik, V., Mishra, A., & Reddy, U. S. (2022). Credit card fraud detection by modelling behaviour pattern using hybrid ensemble model. Arabian Journal for Science and Engineering, 1-11.

Khatri, S., Arora, A., & Agrawal, A. P. (2020, 29-31 Jan. 2020). Supervised Machine Learning Algorithms for Credit Card Fraud Detection: A Comparison. 2020 10th International Conference on Cloud Computing, Data Science & Engineering (Confluence),

Kim, E., Lee, J., Shin, H., Yang, H., Cho, S., Nam, S.-k., Song, Y., Yoon, J.-a., & Kim, J.-i. (2019). Champion-challenger analysis for credit card fraud detection: Hybrid ensemble and deep learning. Expert Systems with Applications, 128, 214-224. https://doi.org/https://doi.org/10.1016/j.eswa.2019.03.042

Lee, N. (2021.02.01). Credit card fraud will increase due to the Covid pandemic, experts warn.

Malik, E. F., Khaw, K. W., Belaton, B., Wong, W. P., & Chew, X. (2022). Credit Card Fraud Detection Using a New Hybrid Machine Learning Architecture. Mathematics, 10(9).

Mathew, J., Pang, C. K., Luo, M., & Leong, W. H. (2017). Classification of imbalanced data by oversampling in kernel space of support vector machines. IEEE transactions on neural networks and learning systems, 29(9), 4065-4076.

Rameem Zahra, S., Ahsan Chishti, M., Iqbal Baba, A., & Wu, F. (2022). Detecting Covid-19 chaos driven phishing/malicious URL attacks by a fuzzy logic and data mining based intelligence system. Egyptian Informatics Journal, 23(2), 197-214. https://doi.org/10.1016/j.eij.2021.12.003

Rb, A., & Kr, S. K. (2021). Credit card fraud detection using artificial neural network. Global Transitions Proceedings, 2(1), 35-41. https://doi.org/10.1016/j.gltp.2021.01.006

Sahin, Y., Bulkan, S., & Duman, E. (2013). A cost-sensitive decision tree approach for fraud detection. Expert Systems with Applications, 40(15), 5916-5923. https://doi.org/https://doi.org/10.1016/j.eswa.2013.05.021

Tyagi, R., Ranjan, R., & Priya, S. (2021). Credit Card Fraud Detection Using Machine Learning Algorithms. 2021 Fifth International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud)(I-SMAC),

Van Vlasselaer, V., Bravo, C., Caelen, O., Eliassi-Rad, T., Akoglu, L., Snoeck, M., & Baesens, B. (2015). APATE: A novel approach for automated credit card transaction fraud detection using network-based extensions. Decision Support Systems, 75, 38-48.

VISAGroup. (2023). VISA secure-online-shopping. https://www.visa.com.au/pay-with-visa/security/secure-online-shopping.html

Vuttipittayamongkol, P., Elyan, E., Petrovski, A., & Jayne, C. (2018, 2018//). Overlap-Based Undersampling for Improving Imbalanced Data Classification. Intelligent Data Engineering and Automated Learning – IDEAL 2018, Cham.

Zakaryazad, A., & Duman, E. (2016). A profit-driven Artificial Neural Network (ANN) with applications to fraud detection and direct marketing. Neurocomputing, 175, 121-131. https://doi.org/https://doi.org/10.1016/j.neucom.2015.10.042
Zareapoor, M., & Shamsolmoali, P. (2015). Application of Credit Card Fraud Detection: Based on Bagging Ensemble Classifier. Procedia Computer Science, 48, 679-685. https://doi.org/https://doi.org/10.1016/j.procs.2015.04.201

Zhang, Y.-F., Lu, H.-L., Lin, H.-F., Qiao, X.-C., & Zheng, H. (2022). The Optimized Anomaly Detection Models Based on an Approach of Dealing with Imbalanced Dataset for Credit Card Fraud Detection. Mobile Information Systems, 2022.

Zhu, X., Ao, X., Qin, Z., Chang, Y., Liu, Y., He, Q., & Li, J. (2021). Intelligent financial fraud detection practices in post-pandemic era. Innovation (Camb), 2(4), 100176. https://doi.org/10.1016/j.xinn.2021.100176

Akkoç, S. (2012). "An empirical comparison of conventional techniques, neural networks and the three stage hybrid Adaptive Neuro Fuzzy Inference System (ANFIS) model for credit scoring analysis: The case of Turkish credit card data." European Journal of Operational Research 222(1): 168-178.

Cherkassky, V. and Y. Ma (2004). "Practical selection of SVM parameters and noise estimation for SVM regression." Neural Networks 17(1): 113-126.

Crook, J. N., et al. (2007). "Recent developments in consumer credit risk assessment." European Journal of Operational Research 183(3): 1447-1465.
Cybersixgill (2020.01.27). "Dark Web Financial Fraud Spikes in Second Half of 2019: Over 76 million Credit Cards for Sale." from https://cybersixgill.com/news/articles/dark-web-financial-fraud-2019.

Lee, N. (2021.02.01). "Credit card fraud will increase due to the Covid pandemic, experts warn." from https://www.cnbc.com/2021/01/27/credit-card-
fraud-is-on-the-rise-due-to-covid-pandemic.html

LUNDUSKI, J. (2022.04.26). "2021 Annual Study: Identity Theft & Credit Card Fraud Has Exploded in Recent Years." Retrieved 11.02, 2023,

Alfaiz, N. S. and S. M. Fati (2022) Enhanced Credit Card Fraud Detection Model Using Machine Learning. Electronics 11, DOI: 10.3390/electronics11040662

Asha, R. and S. K. KR (2021). "Credit card fraud detection using artificial neural network." Global Transitions Proceedings 2(1): 35-41.

Ayorinde, K. (2021). A Methodology for Detecting Credit Card Fraud, Minnesota State University, Mankato.

Bhattacharyya, S., et al. (2011). "Data mining for credit card fraud: A comparative study." Decision Support Systems 50(3): 602-613.

Bontempi, G. (2021). Reproducible machine learning for credit card fraud detection-practical machine learning for credit card fraud detection-practical handbook foreword. May, May.

Breiman, L. (2001). "Random forests." Machine learning 45: 5-32.

Carcillo, F., et al. (2018). "Streaming active learning strategies for real-life credit card fraud detection: assessment and visualization." International Journal of Data Science and Analytics 5: 285-300.

Carcillo, F., et al. (2021). "Combining unsupervised and supervised learning in credit card fraud detection." Information Sciences 557: 317-331.

Carneiro, N., et al. (2017). "A data mining based system for credit-card fraud detection in e-tail." Decision Support Systems 95: 91-101.

Charleonnan, A. (2016). Credit card fraud detection using RUS and MRN algorithms. 2016 Management and Innovation Technology International Conference (MITicon), IEEE.

Dal Pozzolo, A., et al. (2014). "Learned lessons in credit card fraud detection from a practitioner perspective." Expert Systems with Applications 41(10): 4915-4928.

Deepanath, C. and K. Prasad (2019). "IEEE-CIS fraud detection."

Delamaire, L., et al. (2009). "Credit card fraud and detection techniques: a review." Banks and Bank systems 4(2): 57-68.

Dong, Q., et al. (2017). Class rectification hard mining for imbalanced deep learning. Proceedings of the IEEE international conference on computer vision.

Dornadula, V. N. and S. Geetha (2019). "Credit Card Fraud Detection using Machine Learning Algorithms." Procedia Computer Science 165: 631-641.

Forough, J. and S. Momtazi (2021). "Ensemble of deep sequential models for credit card fraud detection." Applied Soft Computing 99: 106883.

Fu, K., et al. (2016). Credit Card Fraud Detection Using Convolutional Neural Networks. Neural Information Processing, Cham, Springer International Publishing.

Gao, J., et al. (2018). Study on Unbalanced Binary Classification with Unknown Misclassification Costs. 2018 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM).

Guanjun, L., et al. (2018). "Random Forest for Credit Card Fraud." IEEE Access.

Itoo, F., et al. (2021). "Comparison and analysis of logistic regression, Naïve Bayes and KNN machine learning algorithms for credit card fraud detection." International Journal of Information Technology 13(4): 1503-1511.

Jurgovsky, J., et al. (2018). "Sequence classification for credit-card fraud detection." Expert Systems with Applications 100: 234-245.

Kalid, S. N., et al. (2020). "A Multiple Classifiers System for Anomaly Detection in Credit Card Data With Unbalanced and Overlapped Classes." IEEE Access 8: 28210-28221.

Karthik, V., et al. (2022). "Credit card fraud detection by modelling behaviour pattern using hybrid ensemble model." Arabian Journal for Science and Engineering: 1-11.

Khatri, S., et al. (2020). Supervised Machine Learning Algorithms for Credit Card Fraud Detection: A Comparison. 2020 10th International Conference on Cloud Computing, Data Science & Engineering (Confluence).

Kim, E., et al. (2019). "Champion-challenger analysis for credit card fraud detection: Hybrid ensemble and deep learning." Expert Systems with Applications 128: 214-224.

Liaw, A. and M. Wiener (2002). "Classification and regression by randomForest." R news 2(3): 18-22.

Malik, E. F., et al. (2022) Credit Card Fraud Detection Using a New Hybrid Machine Learning Architecture. Mathematics 10, DOI: 10.3390/math10091480

Mathew, J., et al. (2017). "Classification of imbalanced data by oversampling in kernel space of support vector machines." IEEE transactions on neural networks and learning systems 29(9): 4065-4076.

Michael, J. and S. L. Gordon (1997). "Data mining technique for marketing, sales and customer support." New York: John Wiley&Sons INC 445: 6.

Olowookere, T. A. and O. S. Adewale (2020). "A framework for detecting credit card fraud with cost-sensitive meta-learning ensemble approach." Scientific African 8: e00464.

Ram, S., et al. (2018). "Devanagri character recognition model using deep convolution neural network." Journal of Statistics and Management Systems 21(4): 593-599.

Randhawa, K., et al. (2018). "Credit Card Fraud Detection Using AdaBoost and Majority Voting." IEEE Access 6: 14277-14284.

Rout, P. K. and B. K. Behera (2021). Goat and Sheep Farming. Sustainability in Ruminant Livestock : Management and Marketing. P. K. Rout and B. K. Behera. Singapore, Springer Singapore: 33-76.

Sahin, Y., et al. (2013). "A cost-sensitive decision tree approach for fraud detection." Expert Systems with Applications 40(15): 5916-5923.
Save, P., et al. (2017). "A novel idea for credit card fraud detection using decision tree." International Journal of Computer Applications 161(13).

Seera, M., et al. (2021). "An intelligent payment card fraud detection system." Annals of operations research: 1-23.

Singh, P. (2017). Comparative study of individual and ensemble methods of classification for credit scoring. 2017 International Conference on Inventive Computing and Informatics (ICICI).

Sohony, I., et al. (2018). Ensemble learning for credit card fraud detection. Proceedings of the ACM India joint international conference on data science and management of data.

Tabachnick, B. G., et al. (2013). Using multivariate statistics, pearson Boston, MA.

Venkatesh, A. and S. G. Jacob (2016). "Prediction of credit-card defaulters: a comparative study on performance of classifiers." International Journal of Computer Applications 145(7).

Vuttipittayamongkol, P., et al. (2018). Overlap-Based Undersampling for Improving Imbalanced Data Classification. Intelligent Data Engineering and Automated Learning – IDEAL 2018, Cham, Springer International Publishing.

Xenopoulos, P. (2017). Introducing DeepBalance: Random deep belief network ensembles to address class imbalance. 2017 IEEE International Conference on Big Data (Big Data), IEEE.

Xia, Y., et al. (2017). "A boosted decision tree approach using Bayesian hyper-parameter optimization for credit scoring." Expert Systems with Applications 78: 225-241.

Zakaryazad, A. and E. Duman (2016). "A profit-driven Artificial Neural Network (ANN) with applications to fraud detection and direct marketing." Neurocomputing 175: 121-131.

Zareapoor, M. and P. Shamsolmoali (2015). "Application of Credit Card Fraud Detection: Based on Bagging Ensemble Classifier." Procedia Computer Science 48: 679-685.

Zhang, X., et al. (2021). "HOBA: A novel feature engineering methodology for credit card fraud detection with a deep learning architecture." Information Sciences 557: 302-316.

Zhang, Y.-F., et al. (2022). "The Optimized Anomaly Detection Models Based on an Approach of Dealing with Imbalanced Dataset for Credit Card Fraud Detection." Mobile Information Systems 2022.

Tyagi, R., et al. (2021). Credit Card Fraud Detection Using Machine Learning Algorithms. 2021 Fifth International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud)(I-SMAC), IEEE.

Vuttipittayamongkol, P., et al. (2018). Overlap-Based Undersampling for Improving Imbalanced Data Classification. Intelligent Data Engineering and Automated Learning – IDEAL 2018, Cham, Springer International Publishing.

指導教授

蔡志豐(Chih-Fong Tsai)

審核日期

2024-6-3

推文