For imbalanced data, classification efficiency degrades significantly due to the missing
information for the positive class, and existing sampling schemes do not consider the …

Ordinal regression (OR) is a crucial in machine learning. Usual assumption is that all
training instances are perfectly denoted. However, when this assumption does not hold, the …

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 …

In order to solve the problem that the traditional spectral clustering algorithm is time-
consuming and resource consuming when applied to large-scale data, resulting in poor …

Learning from imbalanced datasets is a nontrivial task for supervised learning community.
Traditional classifiers may have difficulties to learn the concept related to the minority class …

In this study, we propose an oversampling method called the minority-predictive-probability-
based synthetic minority oversampling technique (MPP-SMOTE) for imbalanced learning …

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 …

An oversampling technique balances a dataset by increasing the number of minority
samples. It is a common and effective method in imbalanced learning. However, most …

Synthetic minority oversampling (SMOTE) has been widely used in dealing with the
imbalance classification in the mechanical fault diagnosis field. However, the classical …

Surface defect detection plays an important role in the quality management of industrial
manufacturing processes. Existing detection methods are developed based on clean and …