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

The class-imbalance problem is an important area that plagues machine learning and data
mining researchers. It is ubiquitous in all areas of the real world. At present, many methods …

Imbalanced sample distribution is usually the main reason for the performance degradation
of machine learning algorithms. Based on this, this study proposes a hybrid framework …

Classification problems of unbalanced data sets are commonplace in industrial production
and medical research fields. Different approaches have been proposed to handle these …

Deep models trained on long-tailed datasets exhibit unsatisfactory performance on tail
classes. Existing methods usually modify the classification loss to increase the learning …

Long-tailed learning is attracting increasing attention due to the unbalanced distributions of
real-world data. The aim is to train well-performing depth models. Traditional knowledge …

To ensure the safe operation of high-speed railways, it is necessary to assess the reliability
of its key components. Among them, as wheels are prone to wear degradation and the wear …

Class imbalance poses a great challenge to traditional classifiers in machine learning as
they strongly favor the majority class while ignoring the minority class. Synthetic over …

Usually the real-world (RW) datasets are imbalanced in nature, ie, there is a significant
difference between the number of negative and positive class samples in the datasets …

Learning class-imbalance data has become a challenging task in machine learning.
Oversampling is an effective way to achieve rebalancing between classes by generating …