این سایت در حال حاضر پشتیبانی نمی شود و امکان دارد داده های نشریات بروز نباشند
Journal of Artificial Intelligence and Data Mining، جلد ۸، شماره ۲، صفحات ۱۴۹-۱۶۰
عنوان فارسی
چکیده فارسی مقاله
کلیدواژه‌های فارسی مقاله
عنوان انگلیسی Credit Card Fraud Detection using Data mining and Statistical Methods
چکیده انگلیسی مقاله Due to today’s advancement in technology and businesses, fraud detection has become a critical component of financial transactions. Considering vast amounts of data in large datasets, it becomes more difficult to detect fraud transactions manually. In this research, we propose a combined method using both data mining and statistical tasks, utilizing feature selection, resampling and cost-sensitive learning for credit card fraud detection. In the first step, useful features are identified using genetic algorithm. Next, the optimal resampling strategy is determined based on the design of experiments (DOE) and response surface methodologies. Finally, the cost sensitive C4.5 algorithm is used as the base learner in the Adaboost algorithm. Using a real-time data set, results show that applying the proposed method significantly reduces the misclassification cost by at least 14% compared with Decision tree, Naïve bayes, Bayesian Network, Neural network and Artificial immune system.
کلیدواژه‌های انگلیسی مقاله
نویسندگان مقاله S. Beigi |
Faculty of Industrial and Systems Engineering, Tarbiat Modares University, Tehran, Iran.

M.R. Amin Naseri |
Industrial Engineering Department, Faculty of basic science and Engineering, Kosar university of Bojnord, Bojnord, Iran.

نشانی اینترنتی http://jad.shahroodut.ac.ir/article_1654_698899faf952d81f56f4efa40c424eb7.pdf
فایل مقاله اشکال در دسترسی به فایل - ./files/site1/rds_journals/480/article-480-2378178.pdf
کد مقاله (doi)
زبان مقاله منتشر شده en
موضوعات مقاله منتشر شده
نوع مقاله منتشر شده
برگشت به: صفحه اول پایگاه   |   نسخه مرتبط   |   نشریه مرتبط   |   فهرست نشریات