Imbalanced data classification remains a focus of intense research, mostly due to the
prevalence of data imbalance in various real-life application domains. A disproportion …

The problem of class imbalance has always been considered as a significant challenge to
traditional machine learning and the emerging deep learning research communities. A …

Class imbalance occurs in classification problems in which the “normal” cases, or instances,
significantly outnumber the “abnormal” instances. Training a standard classifier on …

Fake news can rapidly spread through internet users and can deceive a large audience.
Due to those characteristics, they can have a direct impact on political and economic events …

In this paper we deal with the problem of addressing multi-class problems with
decomposition strategies. Based on the divide-and-conquer principle, a multi-class problem …

Black pepper is a commercially important commodity, which is susceptible for fraudulent
additions. Analytical tools are capable of detection of specific additions, but in most …

Context In host-based anomaly detection, feature extraction on the system call traces is
important to build an effective anomaly detection model. Different kinds of feature extraction …

Machine learning (ML) is playing an increasingly important role in rendering decisions that
affect a broad range of groups in society. ML models inform decisions in criminal justice, the …

Learning classifiers from imbalanced data is known to be a challenging and important prob-
lem in machine learning. As a results, the topic has been studied from a wide variety of …

The class imbalance problem is associated with harmful classification bias and presents
itself in a wide variety of important applications of supervised machine learning. Measures …