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 …

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 …

Without any specific way for imbalance data classification, artificial intelligence algorithm
cannot recognize data from minority classes easily. In general, modifying the existing …

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 …

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 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 …

Oversampling is a widely employed technique for addressing imbalanced datasets, facing
challenges like class overlaps, intra-class imbalance, and noise. In this paper, we introduce …

Class imbalance (CI) in classification problems arises when the number of observations
belonging to one class is lower than the other. Ensemble learning combines multiple models …

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 …