A Comparative Study of Machine Learning Techniques for Credit Card Fraud Detection Based on Time Variance

Rajora, S; Li, DL; Jha, C; Bharill, N; Patel, OP; Joshi, S; Puthal, D; Prasad, M

A Comparative Study of Machine Learning Techniques for Credit Card Fraud Detection Based on Time Variance

Rajora, S Li, DL Jha, C Bharill, N Patel, OP Joshi, S Puthal, D

Prasad, M

Permalink

Publication Type:: Conference Proceeding
Citation:: Proceedings of the 2018 IEEE Symposium Series on Computational Intelligence, SSCI 2018, 2019, pp. 1958 - 1963
Issue Date:: 2019-01-28

Closed Access

	Filename	Description	Size
	08628930.pdf	Published version	282.04 kB	Adobe PDF	View/Open

Copyright Clearance Process

Recently Added
In Progress
Closed Access

This item is closed access and not available.

Full metadata record

Field	Value	Language
dc.contributor.author	Rajora, S	en_US
dc.contributor.author	Li, DL	en_US
dc.contributor.author	Jha, C	en_US
dc.contributor.author	Bharill, N	en_US
dc.contributor.author	Patel, OP	en_US
dc.contributor.author	Joshi, S	en_US
dc.contributor.author	Puthal, D https://orcid.org/0000-0002-8332-278X	en_US
dc.contributor.author	Prasad, M https://orcid.org/0000-0001-9719-2253	en_US
dc.date.issued	2019-01-28	en_US
dc.identifier.citation	Proceedings of the 2018 IEEE Symposium Series on Computational Intelligence, SSCI 2018, 2019, pp. 1958 - 1963	en_US
dc.identifier.isbn	9781538692769	en_US
dc.identifier.uri	http://hdl.handle.net/10453/132207
dc.description.abstract	© 2018 IEEE. This paper proposes a comparative performance of ten different machine learning algorithms, done on a credit card fraud detection application. The machine learning methods have been classified into two groups namely classification algorithms and ensemble learning group. Each group is comprised of five different algorithms. Besides, the 'Time' feature is introduced in the data set and performances of the algorithms are studied with and without the 'Time' feature. Two algorithms of the ensemble learning group have been found to perform better when the used dataset does not include the 'Time' feature. However, for the classification algorithms group, three classifiers are found to show better predictive accuracies when all attributes are included in the used dataset. The rest of the machine learning models have approximate similar scores between these datasets.	en_US
dc.relation.ispartof	Proceedings of the 2018 IEEE Symposium Series on Computational Intelligence, SSCI 2018	en_US
dc.relation.isbasedon	10.1109/SSCI.2018.8628930	en_US
dc.title	A Comparative Study of Machine Learning Techniques for Credit Card Fraud Detection Based on Time Variance	en_US
dc.type	Conference Proceeding
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Information, Systems and Modelling
pubs.organisational-group	/University of Technology Sydney/Strength - CAI - Centre for Artificial Intelligence
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US

Abstract:

© 2018 IEEE. This paper proposes a comparative performance of ten different machine learning algorithms, done on a credit card fraud detection application. The machine learning methods have been classified into two groups namely classification algorithms and ensemble learning group. Each group is comprised of five different algorithms. Besides, the 'Time' feature is introduced in the data set and performances of the algorithms are studied with and without the 'Time' feature. Two algorithms of the ensemble learning group have been found to perform better when the used dataset does not include the 'Time' feature. However, for the classification algorithms group, three classifiers are found to show better predictive accuracies when all attributes are included in the used dataset. The rest of the machine learning models have approximate similar scores between these datasets.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/132207