Nowadays, many classification algorithms have been applied to various industries to help
them work out their problems met in real-life scenarios. However, in many binary …

The class imbalance problem has become a leading challenge. Although conventional
imbalance learning methods are proposed to tackle this problem, they have some …

The dynamic ensemble selection of classifiers is an effective approach for processing label-
imbalanced data classifications. However, such a technique is prone to overfitting, owing to …

Imbalanced classification is one of the most important problems of machine learning and
data mining, existing in many real datasets. In the past, many basic classifiers such as SVM …

In real datasets, most are unbalanced. Data imbalance can be defined as the number of
instances in some classes greatly exceeds the number of instances in other classes …

As one of the most challenging and attractive problems in the pattern recognition and
machine intelligence field, imbalanced classification has received a large amount of …

Learning from imbalanced data, where the number of observations in one class is
significantly rarer than in other classes, has gained considerable attention in the data mining …

The class imbalance problem has posed a leading challenge in real-world applications.
Traditional methods focus on either the data level or algorithm level to solve the binary …

The class imbalance problems have been reported to severely hinder classification
performance of many standard learning algorithms, and have attracted a great deal of …

Ensemble learning by integrating multiple base classifiers that trained different set can
effectively improve the classification accuracy. In the base classifier training process …