  Traditional oversampling methods are generally employed to handle class
imbalance in datasets. This oversampling approach is independent of the
classifier; thus, it does not offer an end-to-end solution. To overcome this,
we propose a three-player adversarial game-based end-to-end method, where a
domain-constraints mixture of generators, a discriminator, and a multi-class
classifier are used. Rather than adversarial minority oversampling, we propose
an adversarial oversampling (AO) and a data-space oversampling (DO) approach.
In AO, the generator updates by fooling both the classifier and discriminator,
however, in DO, it updates by favoring the classifier and fooling the
discriminator. While updating the classifier, it considers both the real and
synthetically generated samples in AO. But, in DO, it favors the real samples
and fools the subset class-specific generated samples. To mitigate the biases
of a classifier towards the majority class, minority samples are over-sampled
at a fractional rate. Such implementation is shown to provide more robust
classification boundaries. The effectiveness of our proposed method has been
validated with high-dimensional, highly imbalanced and large-scale multi-class
tabular datasets. The results as measured by average class specific accuracy
(ACSA) clearly indicate that the proposed method provides better classification
accuracy (improvement in the range of 0.7% to 49.27%) as compared to the
baseline classifier.
