A class imbalance problem occurs when a particular class of data is significantly more or
less than another class of data. This problem is difficult to solve; however, solutions such as …

This paper proposes a novel data augmentation scheme called the conditional generative
adversarial network minority-class-augmented oversampling scheme (CTGAN-MOS) for …

Imbalanced data categorization is inescapable, and it has an impact on the model's
classification problem, which can cause false results. The purpose of this paper is to …

Data imbalance is one of the most difficult problems in machine learning. The improved
ensemble learning model is a promising solution to mitigate this challenge. In this paper, an …

Constructing effective classifiers from imbalanced datasets has emerged as one of the main
challenges in the data mining community, due to its increased prevalence in various real …

Heterogeneity of class distribution is an intrinsic property of a real-world dataset. Therefore,
imbalanced classification is a popular but challenging task. Several methods exist to …

Imbalanced data classification, an important type of classification task, is challenging for
standard learning algorithms. There are different strategies to handle the problem, as …

Class imbalance is a major issue that degrades the performance of machine learning
classifiers in real-world problems. Oversampling methods have been widely used to …

Imbalanced class data distribution occurs when the number of examples representing one
class is much lower than others. This conditioning affects the prediction accuracy degraded …

The class imbalance problem has become a leading challenge. Although conventional
imbalance learning methods are proposed to tackle this problem, they have some …