Class imbalance is often faced by real-world datasets where one class contains a smaller
number of instances than the other one. Even though this has been an area of interest for …

Imbalanced classification problems are often encountered in many applications. The
challenge is that there is a minority class that has typically very little data and is often the …

Real-world datasets are heavily skewed where some classes are significantly outnumbered
by the other classes. In these situations, machine learning algorithms fail to achieve …

Despite more than two decades of progress, learning from imbalanced data is still
considered as one of the contemporary challenges in machine learning. This has been …

Data-driven fault diagnosis techniques are frequently applied to ensure the reliability and
safety of industrial systems. However, as a common challenge, the class-imbalance problem …

Machine learning approaches are widely used to analyze and detect the increasingly
serious problem of credit card fraud. However, typical credit card datasets present …

Learning from imbalanced data is among the most popular topics in the contemporary
machine learning. However, the vast majority of attention in this field is given to binary …

Minority oversampling is currently one of the most popular and effective methods for
handling imbalanced data. However, oversampling that relies on the observations of the …

Imbalanced data classification, as a challenging task, has drawn a significant interest in
numerous scientific areas. One popular strategy to balance the instance quantities between …

The quality of the defect datasets is a critical issue in the domain of software defect
prediction (SDP). These datasets are obtained through the mining of software repositories …