The problem of class imbalance is pervasive across various real-world applications,
resulting in machine learning classifiers exhibiting bias towards majority classes. Algorithm …

Traditional classification tasks suffer from the class-imbalanced problem, where some
classes far outnumber others. To address this issue, existing class-imbalanced learning …

Ensemble learning is one of the main methods used to solve class imbalance problems. In
the traditional ensemble learning algorithm using bagging, the base classifier uses the …

Fracture identification and prediction are of great significance for the production of tight oil
and gas reservoirs. The high angles of fractures limit their traceability and reduce drilling …

Dynamic ensemble has significantly greater potential space to improve the classification of
imbalanced data compared to static ensemble. However, dynamic ensemble schemes are …

Overheating anomaly detection is essential for the quality and reliability of parts produced by
laser powder bed fusion (LPBF) additive manufacturing (AM). In this research, we focus on …

In order to address the class imbalance issue experienced during training for malicious
website detection, we developed a deep generative model based on the diffusion model that …

Class imbalance learning (CIL) has become one of the most challenging research topics. In
this article, we propose a Boosted co‐training method to modify the class distribution so that …

This paper evaluates five strategies for mitigating imbalanced data: oversampling,
undersampling, ensemble methods, specialized algorithms, class weight adjustments, plus …