Meta Learning Algorithms for Credit Card Fraud Detection
نویسندگان
چکیده
Due to the rapid advancement of electronic commerce technology, there is a great and dramatic increase in credit card transactions. As credit card becomes the most popular mode of payment for both online as well as regular purchase, cases of fraud associated with it are also rising; to detect credit card frauds in electronic transactions becomes the focus of risk of control of banks. The proposed work in this paper is the combination of five supervised machine learning algorithms, Classification and Regression Tree (CART), Adaboost and Logitboost,Bagging and Dagging are proposed for classification of credit card data. These resulted forms help researchers to detect fraud in credit card. The experimental result shows the performance analysis of different meta-learning algorithms and also compared on the basis of misclassification and correct classification rate. Smaller misclassification reveals that bagging algorithm performs better classification of Credit card fraud detection technique.
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