Does Adversarial Oversampling Help us?

TL;DR

This paper introduces an end-to-end adversarial oversampling method using a three-player game to improve classification on imbalanced datasets, outperforming traditional oversampling techniques.

Contribution

It proposes a novel three-player adversarial framework with two oversampling strategies, AO and DO, enhancing classifier robustness on imbalanced, high-dimensional data.

Findings

Achieves up to 49.27% accuracy improvement.

Provides more robust classification boundaries.

Effective on large-scale, multi-class tabular datasets.

Abstract

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.