The class-imbalance problem is an important area that plagues machine learning and data
mining researchers. It is ubiquitous in all areas of the real world. At present, many methods …

Minority oversampling is currently one of the most popular and effective methods for
handling imbalanced data. However, oversampling that relies on the observations of the …

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 …

Imbalanced learning constitutes one of the most formidable challenges within data mining
and machine learning. Despite continuous research advancement over the past decades …

Class imbalance poses difficulties in training a classifier that perform well on minority
classes, especially when there is a high imbalance ratio and significant class overlap …

The class imbalance problem is one of the difficult factors affecting the performance of
traditional classifiers. The oversampling technique is the most common way to solve the …

In practical applications, imbalanced datasets significantly degrade the classification
performance of machine learning models. However, most conventional resampling …

In this article, a hypersphere information granule-based fuzzy classifier integrated with Fuzzy
Cognitive Maps (FCM), named FCM-IGFC, is proposed for the classification of imbalanced …

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 …