Class imbalance problems are pervasive in many real-world applications, yet classifying
imbalanced data remains to be a very challenging task in machine learning. SMOTE is the …

In many real-world problems, the datasets are imbalanced when the samples of majority
classes are much greater than the samples of minority classes. In general, machine learning …

In many practical applications, the data are class imbalanced. Accordingly, it is very
meaningful and valuable to investigate the classification of imbalanced data. In the …

The problem of imbalanced data set classification is prevalent in the studies of machine
learning and data mining. In these kinds of data sets, the number of samples in classes is …

Learning from imbalanced data has drawn growing attentions nowadays in the machine
learning and data mining area. The imbalanced distribution will influence the performance of …

Imbalanced data classification, an important type of classification task, is challenging for
standard learning algorithms. There are different strategies to handle the problem, as …

In the field of artificial intelligence, classification algorithms tend to be biased toward the
majority class samples when encountering imbalanced data, resulting in low recognition …

Imbalanced learning is an important branch of machine learning. It addresses the challenge
of improving classifier accuracy for minority classes in imbalanced data sets. Currently, the …

Most existing imbalanced data classification models mainly focus on the classification
performance of majority class samples, and many clustering algorithms need to manually …

In data mining, common classification algorithms cannot effectively learn from imbalanced
data. Oversampling addresses this problem by creating data for the minority class in order to …