  This study examines the impact of class-imbalanced data on deep learning
models and proposes a technique for data balancing by generating synthetic data
for the minority class. Unlike random-based oversampling, our method
prioritizes balancing the informative regions by identifying high entropy
samples. Generating well-placed synthetic data can enhance machine learning
algorithms accuracy and efficiency, whereas poorly-placed ones may lead to
higher misclassification rates. We introduce an algorithm that maximizes the
probability of generating a synthetic sample in the correct region of its class
by optimizing the class posterior ratio. Additionally, to maintain data
topology, synthetic data are generated within each minority sample's
neighborhood. Our experimental results on forty-one datasets demonstrate the
superior performance of our technique in enhancing deep-learning models.
