Binary Imbalanced Data Classification Based on Modified D2GAN Oversampling and Classifier Fusion

Binary Imbalanced Data Classification Based on Modified D2GAN Oversampling and Classifier Fusion

Binary imbalance problem refers to such a classification scenario where one class contains a large number of samples while another class contains only a few samples. When traditional classifiers face with imbalanced datasets, they usually bias towards majority class resulting in poor classification...

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Bibliographic Details
Main Authors: Junhai Zhai, Jiaxing Qi, Sufang Zhang
Format: Article
Language:English
Published: IEEE 2020-01-01
Series:IEEE Access
Subjects:
Binary class imbalance
diversity oversampling
generative adversarial network
classifier fusion
fuzzy integral
Online Access:https://ieeexplore.ieee.org/document/9195865/

Internet

https://ieeexplore.ieee.org/document/9195865/

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