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 …

The class imbalance problem is characterized by an unequal data distribution in which
majority classes have a greater number of data samples than minority classes …

The emerging expanding scope of the Internet of Things (IoT) necessitates robust intrusion
detection systems (IDS) to mitigate security risks effectively. However, existing approaches …

The problem of imbalanced class distribution in real-world datasets severely impairs the
performance of classification algorithms. The learning task becomes more complicated and …

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 …

Class overlap in imbalanced datasets is the most common challenging situation for
researchers in the fields of deep learning (DL) machine learning (ML), and big data (BD) …

Many real-world datasets exhibit imbalanced distributions, in which the majority classes
have sufficient samples, whereas the minority classes often have a very small number of …

Imbalanced classification is a well-known challenge faced by many real-world applications.
This issue occurs when the distribution of the target variable is skewed, leading to a …

The proliferation of malware in recent years has posed a significant threat to the security of
computers and mobile devices. Detecting malware, especially on the Android platform, has …

The long-tailed data distribution is prevalent in real world and it poses great challenge on
deep neural network training. In this paper, we propose Margin-aware Rectified …