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

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 …

Land use and land cover maps provide fundamental information that has been used in
different types of studies, ranging from public health to carbon cycling. However, the existing …

Many real-world data-mining applications involve obtaining predictive models using
datasets with strongly imbalanced distributions of the target variable. Frequently, the least …

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 …

Data preprocessing is a major and essential stage whose main goal is to obtain final data
sets that can be considered correct and useful for further data mining algorithms. This paper …

In many applications, the dataset for classification may be highly imbalanced where most of
the instances in the training set may belong to one of the classes (majority class), while only …

Canonical machine learning algorithms assume that the number of objects in the considered
classes are roughly similar. However, in many real-life situations the distribution of examples …

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 …