Dual Autoencoders Generative Adversarial Network for Imbalanced Classification Problem

Dual Autoencoders Generative Adversarial Network for Imbalanced Classification Problem

The imbalanced classification problem has become greatest issue in many fields, especially in fraud detection. In fraud detection, the transaction datasets available for training are extremely imbalanced, with fraudulent transaction logs considerably less represented. Meanwhile, the feature informat...

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Bibliographic Details
Main Authors: Ensen Wu, Hongyan Cui, Roy E. Welsch
Format: Article
Language:English
Published: IEEE 2020-01-01
Series:IEEE Access
Subjects:
Fraud detection
imbalanced classification
generative adversarial networks
autoencoders
Online Access:https://ieeexplore.ieee.org/document/9093005/

Internet

https://ieeexplore.ieee.org/document/9093005/

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