Real-world data streams pose a unique challenge to the implementation of machine
learning (ML) models and data analysis. A notable problem that has been introduced by the …

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 …

We introduce a new approach for learning forecasting models over large multi-sensor data
sets, including the steps of sliding-window-based feature extraction and rough-set-inspired …

The malicious rumors have tremendously attracted a more substantial number of
researchers to join the fight against the propagation of these types of information in online …

Concept drift is the phenomenon where the data distribution in a data stream changes over
time. It is a ubiquitous problem in the real-world, for example, a traffic accident would cause …

In the context of big data, organizations and individuals can often benefit from the data
mining techniques, such as classification. However, decision-makers must quickly react to …

The embedded, computational and cloud elements of industrial cyber physical systems
(CPS) generate large volumes of data at high velocity to support the operations and …

Machine learning draws its power from various disciplines, including computer science,
cognitive science, and statistics. Although machine learning has achieved great …

Abstract Concept drift has been a widely concerned problem in data analysis and many
important achievements have been obtained. However, there are few discussions on …

Techniques for collecting preferences in the framework of analytical hierarchy process
(AHP) have been widely developed. But the resultant uncertainty has not been paid …