Class-imbalanced datasets are common across different domains such as health, banking,
security and others. With such datasets, the learning algorithms are often biased toward the …

The data imbalance problem in classification is a frequent but challenging task. In real-world
datasets, numerous class distributions are imbalanced and the classification result under …

Class imbalance is a widely existed and challenging problem in real-world applications
such as disease diagnosis, fraud detection, network intrusion detection and so on. Due to …

The fact that image datasets are often imbalanced poses an intense challenge for deep
learning techniques. In this paper, we propose a method to restore the balance in …

Imbalanced sample distribution is usually the main reason for the performance degradation
of machine learning algorithms. Based on this, this study proposes a hybrid framework …

Real-world applications often involve imbalanced datasets, which have different
distributions of examples across various classes. When building a system that requires a …

Imbalanced data distribution is the main reason for the performance degradation of most
supervised classification algorithms. When dealing with imbalanced learning problems, the …

Class imbalance in a dataset is a major problem for classifiers that results in poor prediction
with a high true positive rate (TPR) but a low true negative rate (TNR) for a majority positive …

In data mining, common classification algorithms cannot effectively learn from imbalanced
data. Oversampling addresses this problem by creating data for the minority class in order to …

Learning from imbalanced datasets is a frequent but challenging task for standard
classification algorithms. Although there are different strategies to address this problem …