The purpose of this study is to examine existing deep learning techniques for addressing
class imbalanced data. Effective classification with imbalanced data is an important area of …

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

Many real-world applications reveal difficulties in learning classifiers from imbalanced data.
Although several methods for improving classifiers have been introduced, the identification …

Imbalanced data is a common problem in classification. This phenomenon is growing in
importance since it appears in most real domains. It has special relevance to highly …

Rare objects are often of great interest and great value. Until recently, however, rarity has
not received much attention in the context of data mining. Now, as increasingly complex real …

For large, real-world inductive learning problems, the number of training examples often
must be limited due to the costs associated with procuring, preparing, and storing the …

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 …

It is often assumed that class imbalances are responsible for significant losses of
performance in standard classifiers. The purpose of this paper is to the question whether …

In this article we analyze the effect of class distribution on classifier learning. We begin by
describing the different ways in which class distribution affects learning and how it affects the …

The classifier built from a data set with a highly skewed class distribution generally predicts
the more frequently occurring classes much more often than the infrequently occurring …