Industrial products typically lack defects in smart manufacturing systems, which leads to an
extremely imbalanced task of recognizing surface defects. With this imbalanced sample …

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 …

Imbalanced data distribution is the main reason for the performance degradation of most
supervised classification algorithms. When dealing with imbalanced learning problems, the …

Imbalanced tabular datasets adversely impact the predictive performance of most
supervised learning algorithms as the imbalanced distribution can lead to a bias preferring …

Rockburst is a kind of common geological disaster in deep tunnel engineering. It has the
characteristics of causing great harm and occurring at random locations and times. These …

Numerous imbalanced datasets exist in modern machine learning dilemmas. Challenges of
generalization and fairness stem from the existence of underrepresented classes with …

Oversampling is a widely employed technique for addressing imbalanced datasets, facing
challenges like class overlaps, intra-class imbalance, and noise. In this paper, we introduce …

In recent years, the development of Natural Language Processing (NLP) text-to-face
encoders and Generative Adversarial Networks (GANs) has enabled the synthesis and …

Traditional classification tasks suffer from the class-imbalanced problem, where some
classes far outnumber others. To address this issue, existing class-imbalanced learning …

Multi-scale modeling of items interacting in a sequence of users' historical behaviors in a
sequential recommendation task is crucial. In real scenarios, the user's choice of items …