The dynamicity of real-world systems poses a significant challenge to deployed predictive
machine learning (ML) models. Changes in the system on which the ML model has been …

Spam emails have been traditionally seen as just annoying and unsolicited emails
containing advertisements, but they increasingly include scams, malware or phishing. In …

Abstract Intrusion Detection Systems (IDS) have become pivotal in safeguarding information
systems against evolving threats. Concurrently, Concept Drift presents a significant …

Abstract In the “Big Data” age, the amount and distribution of data have increased wildly and
changed over time in various time-series-based tasks, eg weather prediction, network …

Drift detection methods identify changes in data streams. Such changes are called concept
drifts. Existing drift detection methods often assume that the input is a noise-free data stream …

Learning non-stationary data streams is challenging due to the unique characteristics of
infinite length and evolving property. Current existing works often concentrate on the …

Imbalanced classification is a challenging task in the fields of machine learning and data
mining. Cost-sensitive learning can tackle this issue by considering different …

Financial markets and weather prediction are generating streaming data at a rapid rate. The
frequent concept drifts in these data streams pose significant challenges to learners during …

There is much hope for the positive impact of machine learning on healthcare. In fact,
several ML methods are already used in everyday clinical practice, but the effect of adopting …

Online shopping, already on a steady rise, was propelled even further with the advent of the
COVID-19 pandemic. Of course, credit cards are a dominant way of doing business online …