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

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 …

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 …

Class imbalance problems are pervasive in many real-world applications, yet classifying
imbalanced data remains to be a very challenging task in machine learning. SMOTE is the …

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

Imbalanced data can always be observed in our daily life and various practical tasks. A lot of
well-constructed machine learning methodologies may produce ineffective performance …

Learning classifiers with imbalanced data can be strongly biased toward the majority class.
To address this issue, several methods have been proposed using generative adversarial …

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

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 …