In an actual industrial scenario, machines typically operate normally for the majority of the
time, with malfunctions occurring only occasionally. As a result, there is very little recorded …

Multi-class imbalanced classification is more difficult and less frequently studied than its
binary counterpart. Moreover, research on the causes of the difficulty of multi-class …

Despite more than two decades of progress, learning from imbalanced data is still
considered as one of the contemporary challenges in machine learning. This has been …

Imbalance dataset is one of the challenge in machine learning to predict the correct class
and one state of art solution is Ensemble method. Ensemble method predicts the correct …

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 …

Imbalanced classification is a common issue in Machine Learning, particularly when
misclassifying minor instances leads to significant costs. In literature, various strategies have …

In recent years, deep neural networks (DNNs) have become the de facto models for
practically all visual tasks and most temporal analysis tasks due to the abundance of …

A clustering-based undersampling (CUS) and distance-based near-miss method are widely
used in current imbalanced learning algorithms, but this method has certain drawbacks. In …

Imbalance learning has gained more and more attention from researchers. Most of the
efforts so far have focused on binary imbalance problems, while there are numerous …

Removal of polycyclic aromatic hydrocarbons (PAHs) from wastewater treatment sludge with
appropriate technologies is of great importance for nature and public health. UV technology …