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 …

The Synthetic Minority Oversampling Technique (SMOTE) preprocessing algorithm is
considered" de facto" standard in the framework of learning from imbalanced data. This is …

Most machine learning models are static, but the world is dynamic, and increasing online
deployment of learned models gives increasing urgency to the development of efficient and …

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 …

A challenge to many real-world applications is multiclass imbalance with concept drift. In this
paper, we propose a comprehensive active learning method for multiclass imbalanced …

As our professional, social, and financial existences become increasingly digitized and as
our government, healthcare, and military infrastructures rely more on computer technologies …

Common statistical prediction models often require and assume stationarity in the data.
However, in many practical applications, changes in the relationship of the response and …

Mining and analysing streaming data is crucial for many applications, and this area of
research has gained extensive attention over the past decade. However, there are several …

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