In recent years, an enormous amount of research has been carried out on support vector
machines (SVMs) and their application in several fields of science. SVMs are one of the …

Evolutionary Computation (EC) approaches are inspired by nature and solve optimization
problems in a stochastic manner. They can offer a reliable and effective approach to address …

Class imbalance is an active research area in the machine learning community. However,
existing and recent literature showed that class overlap had a higher negative impact on the …

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

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 …

Imbalanced data with a skewed class distribution are common in many real-world
applications. Deep Belief Network (DBN) is a machine learning technique that is effective in …

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 …

The class imbalance problems have been reported to severely hinder classification
performance of many standard learning algorithms, and have attracted a great deal of …

The class-imbalance problem is an important area that plagues machine learning and data
mining researchers. It is ubiquitous in all areas of the real world. At present, many methods …

The problem of modeling binary responses by using cross-sectional data has been
addressed with a number of satisfying solutions that draw on both parametric and …