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 …

Anomaly detection of gas turbines faces the significant challenges of data imbalance and
inter-class overlap. In this paper, we develop a novel data augmentation method, namely …

Learning class-imbalance data has become a challenging task in machine learning.
Oversampling is an effective way to achieve rebalancing between classes by generating …

The classification problem of imbalanced data is a research focus in machine learning. An
effective method for solving the class-imbalance problem is to generate synthetic samples …

Imbalance classification has received great attention due to its various real-world
applications. Data-level approaches are the most convenient to address data imbalance …

Imbalanced data and label noise are ubiquitous challenges in data mining and machine
learning that severely impair classification performance. The synthetic minority oversampling …

Imbalanced data learning is a ubiquitous challenge in data mining and machine learning. In
particular, the ubiquity and inevitability of noise can exacerbate severe performance …

Oversampling method is used to solve the class imbalanced issues. Some existing
oversampling methods do not well remove noisy samples and avoid synthesizing noisy …

The financial distress detection of listed companies is very important because it can prevent
investors, managers and regulators from suffering huge losses and adverse effects. In this …

The advection–dispersion equation has been widely used to analyze the intermediate field
mixing of pollutants in natural streams. The dispersion coefficient, manipulating the …