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Credit Card Fraud Detection Using AdaBoost and Majority Voting
2018
IEEE Access
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
doi:10.1109/access.2018.2806420
fatcat:htfwvk7egjazvlyhcxqgzshqva