Imbalanced data learning has become a major challenge in data mining and machine
learning. Oversampling is an effective way to re-achieve the balance by generating new …

Synthetic minority oversampling methods have been proven to be an efficient solution for
tackling imbalanced data classification issues. Different strategies have been proposed for …

Abstract Synthetic Minority Over-sampling Technique (SMOTE) is an effective method for
imbalanced data classification. Many variants of SMOTE have been proposed in the past …

Imbalanced data sets contain an unequal distribution of data samples among the classes
and pose a challenge to the learning algorithms as it becomes hard to learn the minority …

Challenges posed by imbalanced data are encountered in many real-world applications.
One of the possible approaches to improve the classifier performance on imbalanced data is …

Traditional machine learning algorithms are always trapped by the class-imbalance problem
due to they are biased to the majority class. As one of the most efficient techniques to solve …

Oversampling is a promising preprocessing technique for imbalanced datasets which
generates new minority instances to balance the dataset. However, improper generated …

An imbalanced data set creates severe problems for the classifier as number of samples of
one class (majority) is much higher than the other class (minority). Synthetic oversampling …

Imbalanced data classification, as a challenging task, has drawn a significant interest in
numerous scientific areas. One popular strategy to balance the instance quantities between …

In recent years, mining with imbalanced data sets receives more and more attentions in both
theoretical and practical aspects. This paper introduces the importance of imbalanced data …