Concept drift describes unforeseeable changes in the underlying distribution of streaming
data overtime. Concept drift research involves the development of methodologies and …

In the Internet age, malware (such as viruses, trojans, ransomware, and bots) has posed
serious and evolving security threats to Internet users. To protect legitimate users from these …

Recently, incremental and on-line learning gained more attention especially in the context of
big data and learning from data streams, conflicting with the traditional assumption of …

As an emerging research topic, online class imbalance learning often combines the
challenges of both class imbalance and concept drift. It deals with data streams having very …

Online class imbalance learning is a new learning problem that combines the challenges of
both online learning and class imbalance learning. It deals with data streams having very …

Data Mining in non-stationary data streams is gaining more attentionrecently, especially in
the context of Internet of Things and Big Data. It is a highly challenging task, since the …

Data stream mining has been receiving increased attention due to its presence in a wide
range of applications, such as sensor networks, banking, and telecommunication. One of the …

Bagging and boosting are well-known ensemble learning methods. They combine multiple
learned base models with the aim of improving generalization performance. To date, they …

Online learning algorithms often have to operate in the presence of concept drifts. A recent
study revealed that different diversity levels in an ensemble of learning machines are …

Advanced analysis of data streams is quickly becoming a key area of data mining research
as the number of applications demanding such processing increases. Online mining when …