Develo** effective and efficient data stream classifiers is challenging for the machine
learning community because of the dynamic nature of data streams. As a result, many data …

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 …

Acquisition, classification, and analysis of seismic data are crucial tasks in volcano
monitoring. The large number of seismic signals that are continuously acquired during the …

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 …

Concept drift is an inevitable problem in non-stationary stream environments, due to
evolving data distributions. In practical applications, multi-stream is more complex than …

Decision trees are commonly used in machine learning since they are accurate and robust
classifiers. After a decision tree is built, the data can change over time, causing the …

O monitoramento vulcânico é importante na mitigação e prevenção de riscos de erupções,
sendo necessária a identificação das fontes internas que geram esse tipo de eventos, bem …