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 …

Many real‐world data mining applications have to deal with unlabeled streaming data. They
are unlabeled because the sheer volume of the stream makes it impractical to label a …

Learning from data streams in the presence of concept drift is among the biggest challenges
of contemporary machine learning. Algorithms designed for such scenarios must take into …

Abstract Concept drift is an important issue in the field of streaming data mining. However,
how to maintain real-time model convergence in a dynamic environment is an important and …

Abstract Concept drift is a common and important issue in streaming data analysis and
mining. Thus far, many concept drift detection methods have been proposed but may not be …

In present times, data science become popular to support and improve decision-making
process. Due to the accessibility of a wide application perspective of data streaming, class …

In many real-world scenarios, we often deal with streaming data that is sequentially
collected over time. Due to the non-stationary nature of the environment, the streaming data …

At present, concept drift in the nonstationary data stream is showing trends with different
speeds and different degrees of severity, which has brought great challenges to many fields …

Recent advances in computer vision have led to a resurgence of interest in visual data
analytics. Researchers are developing systems for effectively and efficiently analyzing visual …

Many daily applications are generating massive amount of data in the form of stream at an
ever higher speed, such as medical data, clicking stream, internet record and banking …