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 …

随着信息技术的快速发展, 各领域的数据正以前所未有的速度产生并被广泛收集和存储,
如何实现数据的智能化处理从而利用数据中蕴含的有价值信息已成为理论和应用的研究热点 …

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 …