Synthetic Information towards Maximum Posterior Ratio for deep learning on Imbalanced Data
This addresses data imbalance for deep learning applications, but it is incremental as it builds on existing synthetic data methods.
The study tackles class-imbalanced data in deep learning by generating synthetic data for the minority class, prioritizing high-entropy samples to improve accuracy, and shows superior performance on 41 datasets.
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.