The Synthetic Minority Oversampling Technique (SMOTE) preprocessing algorithm is
considered" de facto" standard in the framework of learning from imbalanced data. This is …

In practice, as a well-known statistical method, the logistic regression model is used to
evaluate the credit-worthiness of borrowers due to its simplicity and transparency in …

Learning from class-imbalanced data continues to be a common and challenging problem in
supervised learning as standard classification algorithms are designed to handle balanced …

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

Learning and mining from imbalanced datasets gained increased interest in recent years.
One simple but efficient way to increase the performance of standard machine learning …

Although cross-validation is a standard procedure for performance evaluation, its joint
application with oversampling remains an open question for researchers farther from the …

The algorithm of C4. 5 decision tree has the advantages of high classification accuracy, fast
calculation speed and comprehensible classification rules, so it is widely used for medical …

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 …

Classification of imbalanced datasets is a challenging task for standard algorithms. Although
many methods exist to address this problem in different ways, generating artificial data for …

In recent days, deep learning technologies have achieved tremendous success in computer
vision-related tasks with the help of large-scale annotated dataset. Obtaining such dataset …