In imbalanced learning methods, resampling methods modify an imbalanced dataset to form
a balanced dataset. Balanced data sets perform better than imbalanced datasets for many …

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 …

Most existing classification approaches assume the underlying training set is evenly
distributed. In class imbalanced classification, the training set for one class (majority) far …

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 …

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 …

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

This paper presents two adaptive ensemble sampling approaches for imbalanced learning:
one is the undersampling-based approach, and the other one is the oversampling-based …

© International Association of Engineers. Mining imbalanced data, which is also known as
a class imbalanced problem is one of the most enormously challenging tasks in machine …

Class imbalance classification has become a dominant problem in supervised learning. The
bias of majority class instances dominates in quantity over minority class instances in …

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