For the last two decades, oversampling has been employed to overcome the challenge of
learning from imbalanced datasets. Many approaches to solving this challenge have been …

The existence of class imbalance in a dataset can greatly bias the classifier towards majority
classification. This discrepancy can pose a serious problem for deep learning models, which …

Denoising diffusion probabilistic models are becoming the leading generative modeling
paradigm for many important data modalities. Being the most prevalent in the computer …

Heterogeneous tabular data are the most commonly used form of data and are essential for
numerous critical and computationally demanding applications. On homogeneous datasets …

With the continuous emergence of various network attacks, it is becoming more and more
important to ensure the security of the network. Intrusion detection, as one of the important …

While data sharing is crucial for knowledge development, privacy concerns and strict
regulation (eg, European General Data Protection Regulation (GDPR)) unfortunately limit its …

Detecting a large number of attack classes accurately applying machine learning (ML) and
deep learning (DL) techniques depends on the number of representative samples available …

Imbalanced sample distribution is usually the main reason for the performance degradation
of machine learning algorithms. Based on this, this study proposes a hybrid framework …

Data augmentation is an important procedure in deep learning. GAN-based data
augmentation can be utilized in many domains. For instance, in the credit card fraud domain …

The usage of synthetic data is gaining momentum in part due to the unavailability of original
data due to privacy and legal considerations and in part due to its utility as an augmentation …