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

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 …

This survey paper focuses on one of the current primary issues challenging data mining
researchers experimenting on real‐world datasets. The problem is that of imbalanced class …

Big Data applications are emerging during the last years, and researchers from many
disciplines are aware of the high advantages related to the knowledge extraction from this …

Several oversampling methods have been proposed for solving the class imbalance
problem. However, most of them require searching the k-nearest neighbors to generate …

Datasets are growing in size and complexity at a pace never seen before, forming ever
larger datasets known as Big Data. A common problem for classification, especially in Big …

In the domain of machine learning, quality of data is most critical component for building
good models. Predictive analytics is an AI stream used to predict future events based on …

In this paper, we briefly introduce some basic concepts and characteristics of big data. We
are surrounded by massive amount of data but starving for knowledge. In the era of Big …

The negative impact on learning associated with imbalanced proportion of classes has
exploded lately with the exponential growth of “cheap” data. Many real-world problems …

New developments in computation have allowed an explosion for both data generation and
storage. The high value that is hidden within this large volume of data has attracted more …