Nowadays, most real-world datasets suffer from the problem of imbalanced distribution of
data samples in classes, especially when the number of data representing the larger class …

Increasing the number of minority samples by data generation can effectively improve the
performance of mining minority samples using a classifier in imbalanced problems. In this …

Nowadays, the solution to many practical problems relies on machine learning tools.
However, compiling the appropriate training data set for real-world classification problems is …

Many machine learning problem domains, such as the detection of fraud, spam, outliers, and
anomalies, tend to involve inherently imbalanced class distributions of samples. However …

Extreme learning machine (ELM) for single-hidden-layer feedforward neural networks
(SLFN) is a powerful machine learning technique, and has been attracting attentions for its …

Imbalanced datasets are pervasive in classification tasks and would cause degradation of
the performance of classifiers in predicting minority samples. Oversampling is effective in …

Imbalanced learning is one of the most relevant problems in machine learning. However, it
faces two crucial challenges. First, the amount of methods proposed to deal with such …

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 classification, as a challenging task, has drawn a significant interest in
numerous scientific areas. One popular strategy to balance the instance quantities between …

Anomaly detection is a process to identify abnormal behavior that does not confirm the
normal behavior. The abnormal behavior clues are few because it appears rarely. To detect …