Educational data mining is capable of producing useful data-driven applications (eg, early
warning systems in schools or the prediction of students' academic achievement) based on …

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 …

Learning and mining from imbalanced datasets gained increased interest in recent years.
One simple but efficient way to increase the performance of standard machine learning …

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 dataset consists of a majority class and a minority class, where the former's
sample size is substantially larger than other classes. This difference disrupts the data …

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 …

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 …

For imbalanced datasets, the focus of classification is to identify samples of the minority
class. The performance of current data mining algorithms is not good enough for processing …

In data mining, large differences between multi-class distributions regarded as class
imbalance issues have been known to hinder the classification performance. Unfortunately …

The learning of imbalanced datasets is a ubiquitous challenge for researchers in the fields of
data mining and machine learning. Conventional classifiers are often biased towards the …