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 …

The class-imbalanced classification is a difficult problem because not only traditional
classifiers are more biased towards the majority classes and inclined to generate incorrect …

In recent years, most researchers focused on the classification problems of imbalanced data
sets, and these problems are widely distributed in industrial production and medical …

In the field of artificial intelligence, classification algorithms tend to be biased toward the
majority class samples when encountering imbalanced data, resulting in low recognition …

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

Uncontrolled proliferation of B-lymphoblast cells is a common characterization of Acute
Lymphoblastic Leukemia (ALL). B-lymphoblasts are found in large numbers in peripheral …

The class-imbalance problem has been widely distributed in various research fields. The
larger the data scale and the higher the data imbalance, the more difficult the proper …

Class imbalance problems pose significant challenges in the field of data mining. The
skewed distribution of classes in imbalanced datasets often leads conventional classification …

Imbalanced data severely hinder the classification performance of learning-based
algorithms and attract a great deal of attention from researchers. The undersampling method …

Nowadays, many classification algorithms have been applied to various industries to help
them work out their problems met in real-life scenarios. However, in many binary …