The class imbalance problem in classification has been recognized as a significant research problem in recent years and a number of methods have been introduced to improve classification results. Rebalancing class distributions (such as over-sampling or under-sampling of learning datasets) has been popular due to its ease of implementation and relatively good performance. For the Support Vector...

Classification of data is difficult if the data is imbalanced and classes are overlapping. In recent years, more research has started to focus on classification of imbalanced data since real world data is often skewed. Traditional methods are more successful with classifying the class that has the most samples (majority class) compared to the other classes (minority classes). For the classifica...

The class imbalance problem is an important issue in classification of Data mining. For example, in the applications of fraudulent telephone calls, telecommunications management, and rare diagnoses, users would be more interested in the minority than the majority. Although there are many proposed algorithms to solve the imbalanced problem, they are unsuitable to be directly applied on a multire...

Imbalanced data set, a problem often found in real world application, can cause seriously negative effect on classification performance of machine learning algorithms. There have been many attempts at dealing with classification of unbalanced data sets. To handle the problem of imbalanced data is to re balance them artificially by oversampling and/or under-sampling.

Many important learning problems, from a wide variety of domains, involve learning from imbalanced data. Because this learning task is quite challenging, there has been a tremendous amount of research on this topic over the past fifteen years. However, much of this research has focused on methods for dealing with imbalanced data, without discussing exactly how or why such methods work—or what u...

Abstract Machine learning plays an increasingly significant role in the building of Network Intrusion Detection Systems. However, machine models trained with imbalanced cybersecurity data cannot recognize minority data, hence attacks, effectively. One way to address this issue is use resampling, which adjusts ratio between different classes, making more balanced. This research looks at resampli...