Imbalanced learning constitutes one of the most formidable challenges within data mining
and machine learning. Despite continuous research advancement over the past decades …

Power transformers are an indispensable equipment in power transmission and distribution
systems, and failures or hidden defects in power transformers can cause operational and …

The combination of class imbalance and overlap is currently one of the most challenging
issues in machine learning. While seminal work focused on establishing class overlap as a …

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 …

Broad learning system (BLS) is a novel and efficient model, which facilitates representation
learning and classification by concatenating feature nodes and enhancement nodes. In spite …

For over two decades, detecting rare events has been a challenging task among
researchers in the data mining and machine learning domain. Real-life problems inspire …

Clustering analysis is widely used as a pre-process to discover the data distribution for
resampling. Existing clustering-based resampling methods mostly run unsupervised …

Graph neural networks (GNNs) have exhibited superb power in many graph related tasks.
Existing GNNs can be categorized into spatial GNNs and spectral GNNs. The spatial GNNs …

Imbalanced data is ubiquitous in the real world, where there is an uneven distribution of
classes in the datasets. Such class imbalance poses a major challenge for modern deep …

This paper proposes a simple interpolation Oversampling method with the purpose of
Reliably Expanding the Minority class regions (OREM). OREM first finds the candidate …