Virtual sample generation (VSG) technology aims to generate virtual samples based on real
samples, in order to expand the size of the datasets and improve model performance …

Learning from imbalanced datasets is a nontrivial task for supervised learning community.
Traditional classifiers may have difficulties to learn the concept related to the minority class …

In this paper, a Cluster-based Synthetic minority oversampling technique (SMOTE) Both-
sampling (CSBBoost) ensemble algorithm is proposed for classifying imbalanced data. In …

Multiple kernel fuzzy clustering (MKFC) has demonstrated promising performance in
capturing the non-linear relationships within data. However, its effectiveness relies heavily …

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

The synthetic minority oversampling technique (SMOTE) is the most prevalent solution in
class imbalance learning. While SMOTE and its variant methods handle imbalanced data …

When implementing SVMs, two major problems are encountered:(a) the number of local
minima of dual-SVM increases exponentially with the number of samples and (b) the …

Data quality is an essential element necessary for the development of a successful machine-
learning project. One of the biggest challenges in various real-world application domains is …

The synthetic minority oversampling technique (SMOTE) is the most general and popular
solution for imbalanced data. Although SMOTE is effective in solving the class imbalance …

Class imbalance and class overlap create difficulties in the training phase of the standard
machine learning algorithm. Its performance is not well in minority classes, especially when …