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

Despite more than two decades of intense research, learning from imbalanced data still
remains as one of the major difficulties posed for computational intelligence systems. Among …

Classification datasets often have an unequal class distribution among their examples. This
problem is known as imbalanced classification. The Synthetic Minority Over-sampling …

Imbalanced classification problems are often encountered in many applications. The
challenge is that there is a minority class that has typically very little data and is often the …

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

The synthetic minority oversampling technique (SMOTE) algorithm is considered a
benchmark algorithm for addressing the class imbalance learning (CIL) problem. However …

Multiclass imbalance data learning has attracted increasing interests from the research
community. Unfortunately, existing oversampling solutions, when facing this more …

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 …

Developing techniques for the machine learning of a classifier from class-imbalanced data
presents an important challenge. Among the existing methods for addressing this problem …

Imbalanced learning problems contain an unequal distribution of data samples among
different classes and pose a challenge to any classifier as it becomes hard to learn the …