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

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 …

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 …

Imbalanced classification problems are definitely around He and Gracia (2009), and a
successful approach to avoid the overfitting of majority classes is the synthetic generation of …

Addressing imbalanced datasets in classification tasks is a relevant topic in research
studies. The main reason is that for standard classification algorithms, the success rate when …

Imbalance learning is a challenging task for most standard machine learning algorithms.
The Synthetic Minority Oversampling Technique (SMOTE) is a well-known preprocessing …

Inductive learning from multi-class and imbalanced datasets is one of the main challenges
for machine learning. Most machine learning algorithms have their predictive performance …

Imbalanced learning is a challenging task for conventional algorithms. Sampling techniques
address this problem by synthesizing minority class samples or selecting part of majority …

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 …

For the last two decades, oversampling has been employed to overcome the challenge of
learning from imbalanced datasets. Many approaches to solving this challenge have been …