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• W2785637175 abstract "Credit card fraud is a serious problem in financial services. Billions of dollars are lost due to credit card fraud every year. There is a lack of research studies on analyzing real-world credit card data owing to confidentiality issues. In this paper, machine learning algorithms are used to detect credit card fraud. Standard models are first used. Then, hybrid methods which use AdaBoost and majority voting methods are applied. To evaluate the model efficacy, a publicly available credit card data set is used. Then, a real-world credit card data set from a financial institution is analyzed. In addition, noise is added to the data samples to further assess the robustness of the algorithms. The experimental results positively indicate that the majority voting method achieves good accuracy rates in detecting fraud cases in credit cards." @default.
• W2785637175 created "2018-02-23" @default.
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• W2785637175 date "2018-01-01" @default.
• W2785637175 modified "2023-10-17" @default.
• W2785637175 title "Credit Card Fraud Detection Using AdaBoost and Majority Voting" @default.
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• W2785637175 doi "https://doi.org/10.1109/access.2018.2806420" @default.
• W2785637175 hasPublicationYear "2018" @default.
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