The imbalanced data problems in data mining are common nowadays, which occur due to
skewed nature of data. These problems impact the classification process negatively in …

Learning from imbalanced data, where the number of observations in one class is
significantly rarer than in other classes, has gained considerable attention in the data mining …

Nowadays, handling of the imbalance data is a major challenge. Imbalanced data set
means the instances of one class are much more than the instances of another class where …

Imbalanced data problem is widely present in network intrusion detection, spam filtering,
biomedical engineering, finance, science, being a challenge in many real-life data-intensive …

Multi-class imbalance classification problems occur in many real-world applications, which
suffer from the quite different distribution of classes. Decomposition strategies are well …

Traditional approaches tend to cause classier bias in the imbalanced data set, resulting in
poor classification performance for minority classes. In particular, there are many …

For class-imbalance problems, traditional supervised learning algorithms tend to favor
majority instances (also called negative instances). Therefore, it is difficult for them to …

It is extremely significant to identify sleep stages accurately in the diagnosis of obstructive
sleep apnea. In the study, it was aimed at determining sleep and wakefulness using a …

Hangbaiju is highly appreciated flower tea for its health benefits, and its quality and price are
affected by geographical origin. Fast and accurate identification of the geographical origin of …

In this paper, we propose One Class support vector machine (OCSVM) based
undersampling. To demonstrate the effectiveness of the proposed methodology, we worked …