Please use this identifier to cite or link to this item: http://bura.brunel.ac.uk/handle/2438/15835
Title: Credit Card Fraud Detection Using AdaBoost and Majority Voting
Authors: Randhawa, K
Loo, CK
Seera, M
Lim, CP
Nandi, AK
Issue Date: 15-Feb-2018
Citation: Randhawa, K. et al. (2018) 'Credit Card Fraud Detection Using AdaBoost and Majority Voting', IEEE Access, 6, pp. 14277 - 14284. doi: 10.1109/ACCESS.2018.2806420.
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.
URI: https://bura.brunel.ac.uk/handle/2438/15835
DOI: https://doi.org/10.1109/ACCESS.2018.2806420
Other Identifiers: ORCiD: Kuldeep Randhawa https://orcid.org/0000-0002-7025-0887
ORCiD: Chu Kiong Loo https://orcid.org/0000-0001-7867-2665
ORCiD: Manjeevan Seera https://orcid.org/0000-0002-2797-3668
ORCiD: Asoke K. Nandi https://orcid.org/0000-0001-6248-2875
Appears in Collections:Dept of Electronic and Electrical Engineering Research Papers

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