Tabular data, organized like tables with rows and columns, is widely used. Existing models
for tabular data synthesis often face limitations related to data size or complexity. In contrast …

In an actual industrial scenario, machines typically operate normally for the majority of the
time, with malfunctions occurring only occasionally. As a result, there is very little recorded …

The class-imbalance problem is an important area that plagues machine learning and data
mining researchers. It is ubiquitous in all areas of the real world. At present, many methods …

Software defect prediction has caused widespread concern among software engineering
researchers, which aims to erect a software defect prediction model according to historical …

The rapid advancement of big data and artificial intelligence heralds a dual-edged era of
opportunities and challenges for the banking sector. Indeed, enhancing a model's capability …

Air quality spatiotemporal prediction can provide technical support for environmental
governance and sustainable city development. As a classic multi-source spatiotemporal …

Imbalanced data classification is a challenging problem in the field of machine learning.
Class imbalance, class overlap, and large data volume significantly affect classification …

The class imbalance problem is one of the main challenges that hinders classifiers from
identifying unknown instances. When class distribution imbalance and class overlap coexist …

For class-imbalance problems, traditional supervised learning algorithms tend to favor
majority instances (also called negative instances). Therefore, it is difficult for them to …

In a large skewed dataset, the data imbalance is severe and the classifier's accuracy is
biased towards the majority class. Insufficient data makes it challenging for the classifier to …