5553659
doi
10.35940/ijeat.D6815.049420
oai:zenodo.org:5553659
Blue Eyes Intelligence Engineering & Sciences Publication(BEIESP)
Publisher
Nallam Sri Divya
Computer Science and Engineering Department, Sri Manakula Vinayagar Engineering College, Puducherry, India,
P. Sarojini
Computer Science and Engineering Department, Sri Manakula Vinayagar Engineering College, Puducherry, India,
K. Sonika
Computer Science and Engineering Department, Sri Manakula Vinayagar Engineering College, Puducherry, India,
Isolation Forest and Local Outlier Factor for Credit Card Fraud Detection System
V. Vijayakumar
Computer Science and Engineering Department, Sri Manakula Vinayagar Engineering College, Puducherry, India,
issn:2249-8958
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
anomaly detection, isolation, local outlier, fraudulent, credit card
<p>Fraud identification is a crucial issue facing large economic institutions, which has caused due to the rise in credit card payments. This paper brings a new approach for the predictive identification of credit card payment frauds focused on Isolation Forest and Local Outlier Factor. The suggested solution comprises of the corresponding phases: pre-processing of data-sets, training and sorting, convergence of decisions and analysis of tests. In this article, the behavior characteristics of correct and incorrect transactions are to be taught by two kinds of algorithms local outlier factor and isolation forest. To date, several researchers identified different approaches for identifying and growing such frauds. In this paper we suggest analysis of Isolation Forest and Local Outlier Factor algorithms using python and their comprehensive experimental results. Upon evaluating the dataset, we received Isolation Forest with high accuracy compared to Local Outlier Factor Algorithm</p>
Zenodo
2020-04-30
info:eu-repo/semantics/article
5553658
1633614510.772845
370284
md5:28e44b249068863ededcbdf3688f164d
https://zenodo.org/records/5553659/files/D6815049420.pdf
public
2249-8958
Is cited by
issn
International Journal of Engineering and Advanced Technology (IJEAT)
9
4
261-265
2020-04-30