Oversampling is an efficient technique in dealing with class-imbalance problem. It
addresses the problem by reduplicating or generating the minority class samples to balance …

Imbalanced learning problems are a challenge faced by classifiers when data samples have
an unbalanced distribution in each class. Furthermore, the synthetic oversampling method …

SMOTE (Synthetic Minority Oversampling TEchnique) is one of the most popular and well-
known sampling algorithms for addressing class imbalance learning problem. The merits of …

Abstract The Synthetic Minority Over-Sampling Technique (SMOTE) is one of the most well
known methods to solve the unequal class distribution problem in imbalanced datasets …

Minority oversampling techniques have played a pivotal role in the field of imbalanced
learning. While traditional oversampling algorithms can cause problems such as intra-class …

Imbalanced data classification is a challenging issue encountered in many practical
applications. Synthetic minority oversampling technique (SMOTE) and its variants are …

Synthetic over-sampling is a well-known method to solve class imbalance by modifying
class distribution and generating synthetic samples. A large number of synthetic over …

Class imbalance learning is one of the most important topics in the field of machine learning
and data mining, and the Synthetic Minority Oversampling Techniques (SMOTE) is the …

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 …

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 …