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 …

Objective To evaluate the performance of machine learning (ML) algorithms and to compare
them with logistic regression for the prediction of risk of cardiovascular diseases (CVDs) …

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 …

Rationale Aggression, comorbid with neuropsychiatric disorders, exhibits with diverse
clinical presentations and places a significant burden on patients, caregivers, and society …

Abstract Machine learning classifiers perform well on balanced datasets. Unfortunately, a lot
of the real-world data sets are naturally imbalanced. So, imbalanced classification is a …

Context: Software defect prediction (SDP) is an important challenge in the field of software
engineering, hence much research work has been conducted, most notably through the use …

Imbalanced learning is one of the substantial challenging problems in the field of data
mining. The datasets that have skewed class distribution pose hindrance to conventional …

Class imbalance ubiquitously exists in real life, which has attracted much interest from
various domains. Direct learning from imbalanced dataset may pose unsatisfying results …

This paper describes an approach for credit risk evaluation based on linear Support Vector
Machines classifiers, combined with external evaluation and sliding window testing, with …

Data are being generated and used to support all aspects of healthcare provision, from
policy formation to the delivery of primary care services. Particularly, with the change of …