DRBF-DS: Double RBF Kernel-Based Deep Sampling with CNNs to Handle Complex Imbalanced Datasets

The inappropriate distribution of samples is known as the data imbalance problem leading to minority and majority classes, often in the healthcare domain due to weakly labelled data. The challenge of working with these imbalanced datasets is to develop predictive models by augmenting the samples to minority class to equalize to the number of samples of the majority class. The successful integration of double radial basis function kernel with deep sampling and convolutional neural networks is experimented to propose a hybrid sampling method coined as DRBF-DS, which generates extended samples, and augments those to the minority class in this study. In this paper, the proposed DRBF-DS is compared with the variants of simple and widely used probability and non-probability-based sampling strategies. It confirms that the proposed approach outperforms the sampling strategies in terms of accuracy and other performance measures used for validation. Experimental validations are performed on five publicly available complex imbalanced datasets to demonstrate the effectiveness of the proposed DRBF-DS based on various evaluation metrics.