Imbalanced data significantly impacts the efficacy of machine learning models. In cases
where one class greatly outweighs the other in terms of sample count, models might develop …

Learning from imbalanced data is a challenging task. Standard classification algorithms tend
to perform poorly when trained on imbalanced data. Some special strategies need to be …

Imbalanced data, characterized by a substantial difference in data distribution between
majority and minority classes, poses a critical challenge in predictive modeling. This …

Imbalanced data pose a tremendous challenge to standard machine learning classifiers
which assume balanced training data. These methods are inclined to accurately classify the …

Real-world datasets in many domains like medical, intrusion detection, fraud transactions
and bioinformatics are highly imbalanced. In classification problems, imbalanced datasets …

As machine learning practitioners, we often encounter imbalanced datasets in which one
class has considerably fewer instances than the other. Many machine learning algorithms …

Learning from imbalanced data is a challenge that machine learning community is facing
over last decades, due to its ever-growing presence in real-life problems. While there is a …

Imbalanced data are ubiquitous in real-world datasets. This study investigate imbalanced
data distribution for binary classification, ie, where the number of majority class instances is …

This survey paper focuses on one of the current primary issues challenging data mining
researchers experimenting on real‐world datasets. The problem is that of imbalanced class …

Imbalanced data has been the focus of ongoing classification research. It describes a
scenario where the distribution of data samples is uneven, and one or more classes in the …