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

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 …

Imbalanced data severely hinder the classification performance of learning-based
algorithms and attract a great deal of attention from researchers. The undersampling method …

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 …

Learning a classifier from class-imbalance data is an important challenge. Among the
existing solutions, SMOTE has received great praise and features an extensive range of …

Handling an imbalanced class problem is a challenging task in real-world applications. This
problem affects various prediction models that predict only the majority class and fail to …

More often than not, data collected in real-time tends to be imbalanced ie, the samples
belonging to a particular class are significantly more than the others. This degrades the …

Imbalance learning is a challenging task for most standard machine learning algorithms.
The Synthetic Minority Oversampling Technique (SMOTE) is a well-known preprocessing …

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 …