The rising integration of offshore wind energy into the electric grid provides remarkable
opportunities in terms of environmental sustainability and cost efficiency. However, it poses …

Class imbalance occurs when the class distribution is not equal. Namely, one class is under-
represented (minority class), and the other class has significantly more samples in the data …

Imbalanced learning constitutes one of the most formidable challenges within data mining
and machine learning. Despite continuous research advancement over the past decades …

Class imbalance is an active research area in the machine learning community. However,
existing and recent literature showed that class overlap had a higher negative impact on the …

The evolutionary algorithms (EAs) have been shown favorable performance for feature
selection. However, a large number of evaluations are required through the EAs. Thus, they …

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 …

The k-nearest neighbor (kNN) classifier is a classical classification algorithm that has been
applied in many fields. However, the performance of the kNN classifier is limited by a simple …

The k-nearest neighbor (kNN) rule is a classical non-parametric classification algorithm in
pattern recognition, and has been widely used in many fields due to its simplicity …

In the real world, data distributions often exhibit multiple granularities. However, the majority
of existing neighbor-based machine-learning methods rely on manually setting a single …

Clustering analysis is widely used as a pre-process to discover the data distribution for
resampling. Existing clustering-based resampling methods mostly run unsupervised …