Data imbalance in Machine Learning refers to an unequal distribution of classes within a
dataset. This issue is encountered mostly in classification tasks in which the distribution of …

Machine learning (ML) methods are proliferating in scientific research. However, the
adoption of these methods has been accompanied by failures of validity, reproducibility, and …

Most of the traditional pattern classifiers assume their input data to be well-behaved in terms
of similar underlying class distributions, balanced size of classes, the presence of a full set of …

We present a comprehensive suite of experimentation on the subject of learning from
imbalanced data. When classes are imbalanced, many learning algorithms can suffer from …

The class imbalance problem has been recognized in many practical domains and a hot
topic of machine learning in recent years. In such a problem, almost all the examples are …

There has been an increasing interest in tools for automating the annotation of databases.
Machine learning techniques are promising candidates to help curators to, at least, guide …

Access to weblogs, both through commercial services and in academic studies, is usually
limited to the content of the weblog posts. This overlooks an important aspect distinguishing …

This paper deals with the application of support vector machine (SVM) to deal with the class
imbalance problem. The objective of this paper is to examine the feasibility and efficiency of …

As software becomes increasingly important, its quality becomes an increasingly important
issue. Therefore, prior work focused on software quality and proposed many prediction …

This paper is about non-approximate acceleration of high-dimensional nonparametric
operations such as k nearest neighbor classifiers. We attempt to exploit the fact that even if …