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 …

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 …

The class imbalance problem is one of the main challenges that hinders classifiers from
identifying unknown instances. When class distribution imbalance and class overlap coexist …

For class-imbalance problems, traditional supervised learning algorithms tend to favor
majority instances (also called negative instances). Therefore, it is difficult for them to …

The problem of network attacks is a primary focus in the domain of intrusion detection.
Models face significant challenges in recognizing intrusion behaviors, particularly when …

The class imbalance issue is generally intrinsic in multi-label datasets due to the fact that
they have a large number of labels and each sample is associated with only a few of them …

Software defect prediction (SDP) plays a crucial role in ensuring the security and quality of
software systems. However, it faces challenges posed by high-dimensional features present …

Abstract Machine learning models often encounter significant difficulties when dealing with
multiclass imbalanced data streams in nonstationary environments. These challenges can …

Imbalanced datasets can adversely affect classifier performance. Conventional
undersampling approaches may lead to the loss of essential information, while …

Context In the software defect datasets, the number of defective instances is significantly
lower than that of non-defective instances. This imbalance adversely impacts the predictive …