Develo** effective and efficient data stream classifiers is challenging for the machine
learning community because of the dynamic nature of data streams. As a result, many data …

New class detection and effective model expansion are of great importance in incremental
data mining. In open incremental data environments, data often come with novel classes, eg …

In this brief, we investigate the problem of incremental learning under data stream with
emerging new classes (SENC). In the literature, existing approaches encounter the following …

In traditional graph learning tasks, such as node classification, learning is carried out in a
closed-world setting where the number of classes and their training samples are provided to …

Anomaly detection is a longstanding and active research area that has many applications in
domains such as finance, security, and manufacturing. However, the efficiency and …

The critical need for classifying streaming data arises from its widespread use in real-world
industries, where analyzing continuous, dynamic, and evolving data streams accurately and …

Data stream mining presents notable challenges in the form of concept drift and evolution.
Existing learning algorithms, typically designed within a supervised learning framework …

In many real-world applications, data are continuously accumulated within open
environments. For instance, in disease diagnosis, the prevalence of diseases can vary …

Open set classification (OSC) tackles the problem of determining whether the data are in-
class or out-of-class during inference, when only provided with a set of in-class examples at …

We study the problem of active intrusion detection over network traffic streams. Existing
works create clusters for known classes and manually label instances outside the clusters …