Recent advancements in generative approaches in AI have opened up the prospect of
synthetic tabular clinical data generation. From filling in missing values in real-world data …

Southern blight significantly impacts peanut yield, and its severity is exacerbated by high-
temperature and high-humidity conditions. The mycelium attached to the plant's interior …

Generating synthetic samples from the convex space of the minority class is a popular
oversampling approach for imbalanced classification problems. Recently, deep-learning …

The class imbalance problem is one of the difficult factors affecting the performance of
traditional classifiers. The oversampling technique is the most common way to solve the …

Although many methods have been proposed for tackling the class-imbalance problem, they
still suffer from the insufficient feature representative capability and the overfitting problem …

Data-level methods often suffer from mode collapse when the minority class has multiple
distribution patterns. Some studies have tried addressing the problem using similarity …

Dependencies among attributes are a common aspect of tabular data. However, whether
existing tabular data generation algorithms preserve these dependencies while generating …

This paper presents a generative modeling approach called Gmda designed for tabular
data, adapted to its arbitrary feature correlation structure. The generative model is trained so …

Class imbalance presents a significant challenge in machine learning, particularly in
applications where accurate detection of minority classes is essential. This study presents a …

Classifying imbalanced datasets remains a significant challenge for classifiers, with
oversampling techniques being a widely used solution. However, many existing …