Artificial Intelligence (AI) is making a profound impact in almost every domain. A vital enabler
of its great success is the availability of abundant and high-quality data for building machine …

Abstract Transformers are Deep Neural Networks (DNN) that utilize a self-attention
mechanism to capture contextual relationships within sequential data. Unlike traditional …

Deep learning (DL) has emerged as a powerful subset of machine learning (ML) and
artificial intelligence (AI), outperforming traditional ML methods, especially in handling …

Class imbalance occurs when the class distribution is not equal. Namely, one class is under-
represented (minority class), and the other class has significantly more samples in the data …

Class imbalance (CI) in classification problems arises when the number of observations
belonging to one class is lower than the other. Ensemble learning combines multiple models …

Synthetic data consists of artificially generated data. When data are scarce, or of poor
quality, synthetic data can be used, for example, to improve the performance of machine …

The imbalanced learning issue is related to the performance of learning algorithms in the
presence of asymmetrical class distribution. Due to the complex characteristics of …

As acquiring pixel-wise annotations of real-world images for semantic segmentation is a
costly process, a model can instead be trained with more accessible synthetic data and …

With the rise of technology and the continued economic growth evident in modern society,
acts of fraud have become much more prevalent in the financial industry, costing institutions …

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