Since canonical machine learning algorithms assume that the dataset has equal number of
samples in each class, binary classification became a very challenging task to discriminate …

The imbalanced learning issue is related to the performance of learning algorithms in the
presence of asymmetrical class distribution. Due to the complex characteristics of …

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 …

Imbalanced classification problems are often encountered in many applications. The
challenge is that there is a minority class that has typically very little data and is often the …

We present a systematic approach for achieving fairness in a binary classification setting.
While we focus on two well-known quantitative definitions of fairness, our approach …

This textbook explores the different aspects of data mining from the fundamentals to the
complex data types and their applications, capturing the wide diversity of problem domains …

Develo** techniques for the machine learning of a classifier from class-imbalanced data
presents an important challenge. Among the existing methods for addressing this problem …

Class imbalance is one of the most popular and important issues in the domain of
classification. The AdaBoost algorithm is an effective solution for classification, but it still …

Outliers are also referred to as abnormalities, discordants, deviants, or anomalies in the data
mining and statistics literature. In most applications, the data is created by one or more …

Training classifiers with datasets which suffer of imbalanced class distributions is an
important problem in data mining. This issue occurs when the number of examples …