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 …

The vast quantity of information brought by big data as well as the evolving computer
hardware encourages success stories in the machine learning community. In the …

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

The exponential growth in computer networks and network applications worldwide has been
matched by a surge in cyberattacks. For this reason, datasets such as CSE-CIC-IDS2018 …

Abstract Developing Big Data applications has become increasingly important in the last few
years. In fact, several organizations from different sectors depend increasingly on …

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 …

Agricultural Big Data is a set of technologies that allows responding to the challenges of the
new data era. In conjunction with machine learning, farmers can use data to address …

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 …

Computer clusters with the shared-nothing architecture are the major computing platforms
for big data processing and analysis. In cluster computing, data partitioning and sampling …

The massive growth in the scale of data has been observed in recent years being a key
factor of the Big Data scenario. Big Data can be defined as high volume, velocity and variety …