This paper presents a novel adaptive synthetic (ADASYN) sampling approach for learning
from imbalanced data sets. The essential idea of ADASYN is to use a weighted distribution …

In imbalanced learning, most standard classification algorithms usually fail to properly
represent data distribution and provide unfavorable classification performance. More …

Imbalanced data sets contain an unequal distribution of data samples among the classes
and pose a challenge to the learning algorithms as it becomes hard to learn the minority …

Imbalanced learning problems contain an unequal distribution of data samples among
different classes and pose a challenge to any classifier as it becomes hard to learn the …

For imbalanced data sets, examples of minority class are sparsely distributed in sample
space compared with the overwhelming amount of majority class. This presents a great …

An imbalanced data set creates severe problems for the classifier as number of samples of
one class (majority) is much higher than the other class (minority). Synthetic oversampling …

Class imbalance poses a great challenge to traditional classifiers in machine learning as
they strongly favor the majority class while ignoring the minority class. Synthetic over …

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 …

Imbalanced learning is a challenging task for conventional algorithms. Sampling techniques
address this problem by synthesizing minority class samples or selecting part of majority …

Building a classification model on imbalanced datasets can be a challenging endeavor.
Models built on data where examples of one class are greatly outnumbered by examples of …