In machine learning, the data imbalance imposes challenges to perform data analytics in
almost all areas of real-world research. The raw primary data often suffers from the skewed …

In a majority–minority classification problem, class imbalance in the dataset (s) can
dramatically skew the performance of classifiers, introducing a prediction bias for the …

Abstract The advent of Big Data has ushered a new era of scientific breakthroughs. One of
the common issues that affects raw data is class imbalance problem which refers to …

A major issue in the classification of class imbalanced datasets involves the determination of
the most suitable performance metrics to be used. In previous work using several examples …

Learning with imbalanced data refers to the scenario in which the amounts of instances that
represent the concepts in a given problem follow a different distribution. The main issue …

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

Abstract Machine learning plays an increasingly significant role in the building of Network
Intrusion Detection Systems. However, machine learning models trained with imbalanced …

Class imbalance is an active research area in the machine learning community. However,
existing and recent literature showed that class overlap had a higher negative impact on the …

Most evolutionary optimization algorithms assume that the evaluation of the objective and
constraint functions is straightforward. In solving many real-world optimization problems …

The k‐nearest neighbors algorithm is characterized as a simple yet effective data mining
technique. The main drawback of this technique appears when massive amounts of data …