Class imbalance problems pose significant challenges in the field of data mining. The
skewed distribution of classes in imbalanced datasets often leads conventional classification …

Collaborative representation-based classifiers (CRCs) have demonstrated remarkable
classification performance in various pattern recognition fields. However, their success …

Collaborative representation-based classification (CRC) has been extensively applied to
various recognition fields due to its effectiveness and efficiency. Nevertheless, it is generally …

The broad learning system (BLS) has garnered significant attention in the realm of visual
classification due to its exceptional balance between accuracy and efficiency. However, the …

Over-sampling approaches focus on generating samples to balance the dataset and have
been widely applied in classifying imbalanced data. However, existing approaches do not …

Abstract Representation-based methods have found widespread applications in various
classification tasks. However, these methods cannot deal effectively with imbalanced data …

In medical practice, polyp segmentation holds immense significance for early Colorectal
Cancer diagnosis. Over the past decade, techniques based on Deep Learning (DL) have …

Imbalanced classification is an important research direction in machine learning. In this field,
imbalanced data with noise is a challenging problem. Although many methods have been …

Photovoltaic (PV) power generation has become a primary method of energy production due
to its clean and sustainable nature. Therefore, efficient fault detection and classification in …

One-class classification methods are often used for anomaly detection in healthcare, quality
control in manufacturing, and fraud detection in financial services. Particularly in medical …