دانشگاه علامه طباطبایی

  اخبار و اعلانات

 آمار

تعداد نشریات63
تعداد شماره‌ها2,259
تعداد مقالات18,615
تعداد مشاهده مقاله57,668,775
تعداد دریافت فایل اصل مقاله29,522,530
Rezaei Tabar, Vahid, Safakish, Mohaddeseh. (1403). Credit-Card Fraud Detection: Cost-Sensitive Meta-Learning Bayesian Network Classifiers. سامانه مدیریت نشریات علمی, 2(2), 189-215. doi: 10.22054/jdsm.2025.84487.1064
Vahid Rezaei Tabar; Mohaddeseh Safakish. "Credit-Card Fraud Detection: Cost-Sensitive Meta-Learning Bayesian Network Classifiers". سامانه مدیریت نشریات علمی, 2, 2, 1403, 189-215. doi: 10.22054/jdsm.2025.84487.1064
Rezaei Tabar, Vahid, Safakish, Mohaddeseh. (1403). 'Credit-Card Fraud Detection: Cost-Sensitive Meta-Learning Bayesian Network Classifiers', سامانه مدیریت نشریات علمی, 2(2), pp. 189-215. doi: 10.22054/jdsm.2025.84487.1064
Rezaei Tabar, Vahid, Safakish, Mohaddeseh. Credit-Card Fraud Detection: Cost-Sensitive Meta-Learning Bayesian Network Classifiers. سامانه مدیریت نشریات علمی, 1403; 2(2): 189-215. doi: 10.22054/jdsm.2025.84487.1064

Credit-Card Fraud Detection: Cost-Sensitive Meta-Learning Bayesian Network Classifiers

Journal of Data Science and Modeling
دوره 2، شماره 2 - شماره پیاپی 4، شهریور 2024، صفحه 189-215    اصل مقاله (1.76 M)
نوع مقاله: Research Manuscript
شناسه دیجیتال (DOI): 10.22054/jdsm.2025.84487.1064
نویسندگان
Vahid Rezaei Tabar* 1؛ Mohaddeseh Safakish2
1Department of Statistics, Faculty of Statistics, Mathematics and Computer Sciences, Allameh Tabataba'i University, Tehran, Iran
2Department of Statistics, Faculty of Statistics, Mathematics and Computer Sciences, Allameh Tabataba’i University, Tehran, Iran.
چکیده
In the modern era, detecting credit card fraud has become a crucial concern from both financial and security standpoints. Given the rarity of fraudulent activities, the issue is reframed as a binary classification challenge, tackling the complexities of imbalanced datasets. To address this, authors advocate using Bayesian networks due to their theoretical robustness and capacity to model intricate scenarios while maintaining interpretability in the context of class skewed distributions. A pivotal component of this meta learning framework is the cost matrix, leading authors to explore various techniques for its calculation. By employing our meta-learning framework with data from Iran’s banking system, the authors demonstrate a method for determining the cost matrix. Subsequently, develop the corresponding Cost Augmented Bayesian Network Classifiers, called CABNCs. The outcomes highlight the potential of CATAN to diminish financial loss and the effectiveness of CAGHC-K2 in predicting labels for forthcoming transactions in the context of class imbalance.
کلیدواژه‌ها
Bayesian network؛ Classification؛ Cost-sensitive؛ Economic efficiency؛ Fraud detection؛ Meta-learning
آمار
تعداد مشاهده مقاله: 556
تعداد دریافت فایل اصل مقاله: 267


سامانه مدیریت نشریات علمی. طراحی و پیاده سازی از سیناوب
login