Genotypic divergence is a term adapted from population genetics and intimately linked to
evolution. We use divergence here to emphasize the differences that set individuals apart in …

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 …

The multilabel learning task aims to predict the associated multiple classes of a given
example simultaneously. Such task becomes more challenging when data arrives in stream …

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

Online clustering of short text streams has become significant due to the popularity of news
and social media platforms. The objective of online clustering is to maintain active topics …

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 …

In dynamic environments, making classification decisions based on classical intelligent
decision support systems is a challenge, as the classification performance of decision …

Traditional statistical methods are faced with new challenges due to streaming data. The
major challenge is the rapidly growing volume and velocity of data, which makes storing …

Genotypic divergence is a term adapted from population genetics and intimately linked to
evolution. We use divergence here to emphasize the differences that set individuals apart in …