Class imbalance problem (CIP) in a dataset is a major challenge that significantly affects the
performance of Machine Learning (ML) models resulting in biased predictions. Numerous …

Without any specific way for imbalance data classification, artificial intelligence algorithm
cannot recognize data from minority classes easily. In general, modifying the existing …

In this paper, we propose a novel oversampling strategy dubbed Entropy-based
Wasserstein Generative Adversarial Network (EWGAN) to generate data samples for …

The fact that image datasets are often imbalanced poses an intense challenge for deep
learning techniques. In this paper, we propose a method to restore the balance in …

Oversampling is a widely employed technique for addressing imbalanced datasets, facing
challenges like class overlaps, intra-class imbalance, and noise. In this paper, we introduce …

Generative adversarial network (GAN) is an overwhelming yet promising method to address
the data imbalance problem. However, most existing GANs that are usually inspired by …

In this paper, we propose a discriminative variational autoencoder (DVAE) to assist deep
learning from data with imbalanced class distributions. DVAE is designed to alleviate the …

In many practical applications, the data are class imbalanced. Accordingly, it is very
meaningful and valuable to investigate the classification of imbalanced data. In the …

Class-imbalanced datasets are common across several domains such as health, banking,
security, and others. The dominance of majority class instances (negative class) often results …

Class imbalance in a dataset is a major problem for classifiers that results in poor prediction
with a high true positive rate (TPR) but a low true negative rate (TNR) for a majority positive …