The imbalanced learning issue is related to the performance of learning algorithms in the
presence of asymmetrical class distribution. Due to the complex characteristics of …

Many real-world data-mining applications involve obtaining predictive models using
datasets with strongly imbalanced distributions of the target variable. Frequently, the least …

With the continuous emergence of various network attacks, it is becoming more and more
important to ensure the security of the network. Intrusion detection, as one of the important …

Most existing classification approaches assume the underlying training set is evenly
distributed. In class imbalanced classification, the training set for one class (majority) far …

With the continuous expansion of data availability in many large-scale, complex, and
networked systems, such as surveillance, security, Internet, and finance, it becomes critical …

Traditional classification algorithms usually provide poor accuracy on the prediction of the
minority class of imbalanced data sets. This paper proposes a new method for dealing with …

Imbalanced sample distribution is usually the main reason for the performance degradation
of machine learning algorithms. Based on this, this study proposes a hybrid framework …

The present paper investigates the influence of both the imbalance ratio and the classifier on
the performance of several resampling strategies to deal with imbalanced data sets. The …

Support vector machines (SVMs) is a popular machine learning technique, which works
effectively with balanced datasets. However, when it comes to imbalanced datasets, SVMs …

Word embeddings that consider context have attracted great attention for various natural
language processing tasks in recent years. In this paper, we utilize contextualized word …