In Synthetic Minority Over-sampling TEchnique (SMOTE), noise samples may participate in
the synthesis of new samples, so it is difficult to guarantee the rationality of the new samples …

The synthetic minority oversampling technique (SMOTE) has emerged as a significant
approach to address class imbalance challenges in machine learning. However, the …

The phenomenon of data imbalance is very common in real life. In order to improve the
overall classification accuracy, classifiers often misclassify minority class at the cost. But in …

The phenomenon of data imbalance is very common in real life. In order to improve the
overall classification accuracy, classifiers often misclassify minority class at the cost. But in …

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 …

The problem of data imbalance is common in reality, which greatly affects the performance
of classifiers. Most of the solutions are to balance the data set by generating new minority …

Aiming at the low classification accuracy of unbalanced datasets, an unbalanced data
classification algorithm based on improved SMOTE (Synthetic Minority Oversampling …

In imbalanced datasets, the oversampling algorithm, such as Smote (Synthetic Minority
Oversampling) algorithm, R-Smote algorithm and SD-ISmote algorithm, may blur the …

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 …

With the development and progress of machine learning, classification algorithms are
commonly used. One of the main factors that affect classification algorithms is imbalanced …