Analyzing satellite images and remote sensing (RS) data using artificial intelligence (AI)
tools and data fusion strategies has recently opened new perspectives for environmental …

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

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 …

The problem of imbalanced data classification often exists in medical diagnosis. Traditional
classification algorithms usually assume that the number of samples in each class is similar …

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 …

Imbalance in dataset enforces numerous challenges to implement data analytic in all
existing real world applications using machine learning. Data imbalance occurs when …

Stroke is enlisted as one of the leading causes of death and serious disability affecting
millions of human lives across the world with high possibilities of becoming an epidemic in …

Bank credit risk is a significant challenge in modern financial transactions, and the ability to
identify qualified credit card holders among a large number of applicants is crucial for the …

Classifiers trained with class-imbalanced data are known to perform poorly on test data of
the" minor" classes, of which we have insufficient training data. In this paper, we investigate …

In many real-world applications classification problems suffer from class-imbalance. The
classification methods for imbalanced data with only data processing or algorithm …