  The present study aimed to address the issue of imbalanced data in
classification tasks and evaluated the suitability of SMOTE, ADASYN, and GAN
techniques in generating synthetic data to address the class imbalance and
improve the performance of classification models in low-resource settings. The
study employed the Generalised Linear Model (GLM) algorithm for class balancing
experiments and the Random Forest (RF) algorithm for low-resource setting
experiments to assess model performance under varying training data. The recall
metric was the primary evaluation metric for all classification models. The
results of the class balancing experiments showed that the GLM model trained on
GAN-balanced data achieved the highest recall value. Similarly, in low-resource
experiments, models trained on data enhanced with GAN-synthesized data
exhibited better recall values than original data. These findings demonstrate
the potential of GAN-generated synthetic data for addressing the challenge of
imbalanced data in classification tasks and improving model performance in
low-resource settings.
