For the last two decades, oversampling has been employed to overcome the challenge of
learning from imbalanced datasets. Many approaches to solving this challenge have been …

In recent years, class imbalance learning (CIL) has become an important branch of machine
learning. The Synthetic Minority Oversampling TEchnique (SMOTE) is considered to be a …

In this paper, a novel, parameter-free, incremental local density and cluster-based outlier
factor (iLDCBOF) method is presented that unifies incremental versions of local outlier factor …

Node classification for highly imbalanced graph data is challenging, with existing graph
neural networks (GNNs) typically utilizing a balanced class distribution to learn node …

The problem of class imbalance has always been considered as a significant challenge to
traditional machine learning and the emerging deep learning research communities. A …

Since most classifiers are biased toward the dominant class, class imbalance is a
challenging problem in machine learning. The most popular approaches to solving this …

Class imbalance learning is one of the most important topics in the field of machine learning
and data mining, and the Synthetic Minority Oversampling Techniques (SMOTE) is the …

Data-driven transient stability prediction (TSP) exists with issues of model robustness and
sample imbalance. An instance-based and parameter-based of hybrid transfer learning …