There has been growing attention to multi-class classification problems, particularly those
challenges of imbalanced class distributions. To address these challenges, various …

Despite over two decades of progress, imbalanced data is still considered a significant
challenge for contemporary machine learning models. Modern advances in deep learning …

Artificial intelligence (AI), and especially its sub-field of Machine Learning (ML), are
impacting the daily lives of everyone with their ubiquitous applications. In recent years, AI …

Recent years have witnessed the success of Few-shot Learning (FSL) methods in
equipment reliability enhancement and fault diagnosis, by virtue of learning from limited data …

The class imbalance problem is characterized by an unequal data distribution in which
majority classes have a greater number of data samples than minority classes …

In numerous classification problems, class distribution is not balanced. For example, positive
examples are rare in the fields of disease diagnosis and credit card fraud detection. General …

Multi-class imbalanced classification is more difficult and less frequently studied than its
binary counterpart. Moreover, research on the causes of the difficulty of multi-class …

Many binary class datasets in real-life applications are affected by class imbalance problem.
Data complexities like noise examples, class overlap and small disjuncts problems are …

The problem of class imbalance has always been considered as a significant challenge to
traditional machine learning and the emerging deep learning research communities. A …

Node classification for highly imbalanced graph data is challenging, with existing graph
neural networks (GNNs) typically utilizing a balanced class distribution to learn node …