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 …

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 …

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 …

Imbalanced class distribution is a challenging problem in many real-life classification
problems. Existing synthetic oversampling do suffer from the curse of dimensionality …

The learning problem from imbalanced data set poses a major challenge in data mining
community. Conventional machine learning algorithms show poor performance in dealing …

Imbalanced data learning has become a major challenge in data mining and machine
learning. Oversampling is an effective way to re-achieve the balance by generating new …

Synthetic over-sampling is a well-known method to solve class imbalance by modifying
class distribution and generating synthetic samples. A large number of synthetic over …

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 …

Challenges posed by imbalanced data are encountered in many real-world applications.
One of the possible approaches to improve the classifier performance on imbalanced data is …

In recent years, mining with imbalanced data sets receives more and more attentions in both
theoretical and practical aspects. This paper introduces the importance of imbalanced data …