Leveraging mixture of experts and deep learning-based data rebalancing to improve credit fraud detection

Yang, Z., Wang, Y., Shi, H. et al. (1 more author) (2024) Leveraging mixture of experts and deep learning-based data rebalancing to improve credit fraud detection. Big Data and Cognitive Computing, 8 (11). p. 151. ISSN 2504-2289

Abstract

Metadata

Item Type: Article
Authors/Creators:
  • Yang, Z.
  • Wang, Y.
  • Shi, H.
  • Qiu, Q.
Copyright, Publisher and Additional Information:

© 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).

Keywords: credit card fraud detection; financial security; mixture of experts; ensembleearning; synthetic data generation
Dates:
  • Published: November 2024
  • Published (online): 5 November 2024
  • Accepted: 29 October 2024
  • Submitted: 13 August 2024
Institution: The University of Sheffield
Academic Units: The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Computer Science (Sheffield)
Depositing User: Symplectic Sheffield
Date Deposited: 18 Dec 2024 12:18
Last Modified: 18 Dec 2024 12:18
Status: Published
Publisher: MDPI AG
Refereed: Yes
Identification Number: 10.3390/bdcc8110151
Related URLs:
Open Archives Initiative ID (OAI ID):

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