Publication: Comparative Analysis of Methods for Detecting Fraudulent Transactions
Дата
2021
Авторы
Sereda, T. E.
Kondratev, I. A.
Bazanov, V. V.
Uskov, D. A.
Kuchebo, A. V.
Усков, Даниил Алексеевич
Journal Title
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Volume Title
Издатель
Аннотация
© 2021 IEEE.This article is focused on the comparative analysis of machine learning models used to identify fraudulent transactions. During the research, models of three different algorithms were considered, and their optimization for the task was performed. The accuracy of the models was compared, the advantages and disadvantages of each model were identified, recommendations for their use were given, and conclusions were drawn. The experiment was conducted to evaluate the effectiveness of various machine learning models in transaction classification problems.
Описание
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Цитирование
Comparative Analysis of Methods for Detecting Fraudulent Transactions / Sereda, T.E. [et al.] // Proceedings of the 2021 IEEE Conference of Russian Young Researchers in Electrical and Electronic Engineering, ElConRus 2021. - 2021. - P. 468-472. - 10.1109/ElConRus51938.2021.9396074
URI
https://www.doi.org/10.1109/ElConRus51938.2021.9396074
https://www.scopus.com/record/display.uri?eid=2-s2.0-85104715434&origin=resultslist
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https://openrepository.mephi.ru/handle/123456789/24051
https://www.scopus.com/record/display.uri?eid=2-s2.0-85104715434&origin=resultslist
http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcAuth=Alerting&SrcApp=Alerting&DestApp=WOS_CPL&DestLinkType=FullRecord&UT=WOS:000669709800105
https://openrepository.mephi.ru/handle/123456789/24051