Generative adversarial network (GAN) is an overwhelming yet promising method to address
the data imbalance problem. However, most existing GANs that are usually inspired by …

Power transformers are an indispensable equipment in power transmission and distribution
systems, and failures or hidden defects in power transformers can cause operational and …

The combination of class imbalance and overlap is currently one of the most challenging
issues in machine learning. While seminal work focused on establishing class overlap as a …

Abstract Imbalanced learning (IL), ie, learning unbiased models from class-imbalanced data,
is a challenging problem. Typical IL methods including resampling and reweighting were …

Class imbalance is a common issue in the community of machine learning and data mining.
The class-imbalance distribution can make most classical classification algorithms neglect …

Broad learning system (BLS) is a novel and efficient model, which facilitates representation
learning and classification by concatenating feature nodes and enhancement nodes. In spite …

Given a base classifier such as the support vector classifier, the self-training algorithm can
be used to build a high-performance classification model to detect mineralization anomalies …

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

Previous studies have shown that the performance of a classifier on imbalanced data
heavily relies on informative objects lying in borderline or overlap** areas. In this study …

XGBoost is one of the popular machine learning models used in the domains like fraud
detection as well as to tackle the class imbalance that creates overfitting if not handled …