Credit card fraud detection using AdaBoost and majority voting

Randhawa, Kuldeep, Loo, Chu Kiong, Seera, Manjeevan, Lim, Chee Peng and Nandi, Asoke K 2018, Credit card fraud detection using AdaBoost and majority voting, IEEE access, vol. 6, pp. 14277-14284, doi: 10.1109/ACCESS.2018.2806420.

Attached Files
Name Description MIMEType Size Downloads

Title Credit card fraud detection using AdaBoost and majority voting
Author(s) Randhawa, Kuldeep
Loo, Chu Kiong
Seera, Manjeevan
Lim, Chee PengORCID iD for Lim, Chee Peng
Nandi, Asoke K
Journal name IEEE access
Volume number 6
Start page 14277
End page 14284
Total pages 8
Publisher Institute of Electrical and Electronics Engineers
Place of publication Piscataway, N.J.
Publication date 2018
ISSN 2169-3536
Keyword(s) AdaBoost
credit card
fraud detection
predictive modelling
Summary 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.
Language eng
DOI 10.1109/ACCESS.2018.2806420
HERDC Research category C1 Refereed article in a scholarly journal
Copyright notice ©2018, IEEE
Persistent URL

Document type: Journal Article
Collections: Centre for Intelligent Systems Research
Open Access Checking
Connect to link resolver
Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved.

Version Filter Type
Citation counts: TR Web of Science Citation Count  Cited 0 times in TR Web of Science
Scopus Citation Count Cited 11 times in Scopus
Google Scholar Search Google Scholar
Access Statistics: 26 Abstract Views, 0 File Downloads  -  Detailed Statistics
Created: Thu, 12 Jul 2018, 16:06:13 EST

Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact