Evolutionary Computation (EC) approaches are inspired by nature and solve optimization
problems in a stochastic manner. They can offer a reliable and effective approach to address …

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 …

Despite over two decades of progress, imbalanced data is still considered a significant
challenge for contemporary machine learning models. Modern advances in deep learning …

Rare events, especially those that could potentially negatively impact society, often require
humans' decision-making responses. Detecting rare events can be viewed as a prediction …

Despite more than two decades of continuous development learning from imbalanced data
is still a focus of intense research. Starting as a problem of skewed distributions of binary …

Data for face analysis often exhibit highly-skewed class distribution, ie, most data belong to
a few majority classes, while the minority classes only contain a scarce amount of instances …

Empirical evidences linking users' psychological features such as personality traits and
cybercrimes such as cyberbullying are many. This study deals with automatic cyberbullying …

Abstract Multiple Classifier Systems (MCS) have been widely studied as an alternative for
increasing accuracy in pattern recognition. One of the most promising MCS approaches is …

Abstract Predictive performance of Machine Learning (ML) models rely on the quality of data
used for training the models. However, if the training data is not balanced among different …

Classification of imbalanced data is a vastly explored issue of the last and present decade
and still keeps the same importance because data are an essential term today and it …