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

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 …

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 …

SMOTE is a well-known oversampling method for learning on imbalanced datasets.
However, it has the risk of introducing noisy instances and overfitting problems. In order to …

In this study, we propose an oversampling method called the minority-predictive-probability-
based synthetic minority oversampling technique (MPP-SMOTE) for imbalanced learning …

Imbalanced datasets are commonly encountered in real-world classification problems.
However, many machine learning algorithms are originally designed for well-balanced …

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

Imbalanced learning is a challenging task for conventional algorithms. Sampling techniques
address this problem by synthesizing minority class samples or selecting part of majority …

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 …

Imbalanced datasets are pervasive in classification tasks and would cause degradation of
the performance of classifiers in predicting minority samples. Oversampling is effective in …