Data imbalance in Machine Learning refers to an unequal distribution of classes within a
dataset. This issue is encountered mostly in classification tasks in which the distribution of …

The purpose of this study is to examine existing deep learning techniques for addressing
class imbalanced data. Effective classification with imbalanced data is an important area of …

In parallel with the rapid adoption of artificial intelligence (AI) empowered by advances in AI
research, there has been growing awareness and concerns of data privacy. Recent …

Real-world classification problems typically exhibit an imbalanced or long-tailed label
distribution, wherein many labels are associated with only a few samples. This poses a …

Deep classifiers have achieved great success in visual recognition. However, real-world
data is long-tailed by nature, leading to the mismatch between training and testing …

Node classification is an important research topic in graph learning. Graph neural networks
(GNNs) have achieved state-of-the-art performance of node classification. However, existing …

Learning discriminative image representations plays a vital role in long-tailed image
classification because it can ease the classifier learning in imbalanced cases. Given the …

Abstract The advent of Big Data has ushered a new era of scientific breakthroughs. One of
the common issues that affects raw data is class imbalance problem which refers to …

In machine learning, the data imbalance imposes challenges to perform data analytics in
almost all areas of real-world research. The raw primary data often suffers from the skewed …

Background Data augmentation (DA) has recently been demonstrated to achieve
considerable performance gains for deep learning (DL)—increased accuracy and stability …