  One of the most studied machine learning challenges that recent studies have
shown the susceptibility of deep neural networks to is the class imbalance
problem. While concerted research efforts in this direction have been notable
in recent years, findings have shown that the canonical learning objective,
empirical risk minimization (ERM), is unable to achieve optimal imbalance
learning in deep neural networks given its bias to the majority class. An
alternative learning objective, group distributionally robust optimization
(gDRO), is investigated in this study for imbalance learning, focusing on
tabular imbalanced data as against image data that has dominated deep imbalance
learning research. Contrary to minimizing average per instance loss as in ERM,
gDRO seeks to minimize the worst group loss over the training data.
Experimental findings in comparison with ERM and classical imbalance methods
using four popularly used evaluation metrics in imbalance learning across
several benchmark imbalance binary tabular data of varying imbalance ratios
reveal impressive performance of gDRO, outperforming other compared methods in
terms of g-mean and roc-auc.
