Article citationsMore>>
Galar, M., Fernandez, A., Barrenechea, E., Bustince, H., & Herrera, F. (2012). A Review on Ensembles for the Class Imbalance Problem: Bagging-, Boosting-, and Hybrid-Based Approaches. IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews, 42, 463-484.
https://doi.org/10.1109/TSMCC.2011.2161285
has been cited by the following article:
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TITLE:
Adaptive Financial Fraud Detection in Imbalanced Data with Time-Varying Poisson Processes
AUTHORS:
Régis Houssou, Jérôme Bovay, Stephan Robert
KEYWORDS:
Homogeneous Poisson Process, Inhomogeneous Poisson Process, Intensity Model, Fraud Detection, Imbalanced Data
JOURNAL NAME:
Journal of Financial Risk Management,
Vol.8 No.4,
December
30,
2019
ABSTRACT: This paper discusses financial fraud detection in imbalanced dataset using homogeneous and non-homogeneous Poisson processes. The probability of predicting fraud on the financial transaction is derived. Applying our methodology to financial datasets with different fraud profiles shows a better predicting power than a baseline approach, especially in the case of higher imbalanced data.
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