Class imbalance is one of the challenging problems for machine learning in many real-world
applications. Many methods have been proposed to address and attempt to solve the …

Class imbalance is an active research area in the machine learning community. However,
existing and recent literature showed that class overlap had a higher negative impact on the …

Class-imbalance learning deals with the problem where the number of training data in one
class is significantly more than another class. To tackle this problem, some feasible and …

Traditional classification algorithms can be limited in their performance on imbalanced
datasets. In recent years, the imbalanced data learning problem has drawn significant …

The performance of traditional classification algorithms can be limited on imbalanced
datasets. In recent years, the imbalanced data learning problem has drawn significant …

Learning from imbalanced data sets is one of the challenging problems in machine learning,
which means the number of negative examples is far more than that of positive examples …

With the expansion of data, increasing imbalanced data has emerged. When the imbalance
ratio of data is high, most existing imbalanced learning methods decline in classification …

Class imbalance is a frequently occurring issue in predictive modeling. Learning from
imbalanced data is a challenging task that has attracted much interest from scholars. While a …

The imbalanced learning issue is related to the performance of learning algorithms in the
presence of asymmetrical class distribution. Due to the complex characteristics of …

Class imbalance learning tackles supervised learning problems where some classes have
significantly more examples than others. Most of the existing research focused only on …