In recent years, the availability of time series streaming information has been growing
enormously. Learning from real-time data has been receiving increasingly more attention …

Practical applications involving big data, such as weather monitoring, identification of
customer preferences, Internet log analysis, and sensors warnings require challenging data …

Streaming data analysis has drawn much attention, where large amounts of data arrive in
streams. Because limited memory can only store a small batch of data, fast analysis without …

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 may lead to a sharp downturn in the performance of streaming in data-
based algorithms, caused by unforeseeable changes in the underlying distribution of data …

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 …

The data stream poses additional challenges to statistical classification tasks because
distributions of the training and target samples may differ as time passes. Such a distribution …

We present a novel method for concept drift detection, based on: 1) the development and
continuous updating of online sequential extreme learning machines (OS-ELMs) and 2) the …

The feasibility of deep neural networks (DNNs) to address data stream problems still
requires intensive study because of the static and offline nature of conventional deep …

Deep learning techniques have become one of the main propellers for solving engineering
problems effectively and efficiently. For instance, Predictive Maintenance methods have …