Class imbalance is a prevalent problem in many real-world applications, and imbalanced
data distribution can dramatically skew the performance of classifiers. In general, the higher …

Within‐class imbalance problems often occur in imbalanced data classification, which
worsen the imbalance distribution problem and increase the learning concept complexity …

Imbalanced data classification poses a major challenge in data mining community. Although
standard support vector machine can generally show relatively robust performance in …

Support vector machine (SVM) is a powerful tool for pattern classification and regression
estimation. However, for the class imbalanced problem, conventional SVMs are not suitable …

Class imbalance problem occurs when the number of training instances belonging to
different classes are clearly different. In this scenario, many traditional classifiers often fail to …

Class imbalances have been reported to compromise the performance of most standard
classifiers, such as Naive Bayes, Decision Trees and Neural Networks. Aiming to solve this …

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 …

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 …

Classification of data with imbalanced class distributions is a major problem in the data
mining community. Imbalanced classification is a challenging task in the presence of …

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 …