Supervised data stream learning depends on the incoming sample's true label to update a
classifier's model. In real life, obtaining the ground truth for each instance is a challenging …

In the widespread application of industrial defect detection supported by neural networks,
changes in the characteristics of industrial site data affect the model performance. To …

Cities serve as vital hubs of economic activity and knowledge generation and dissemination.
As such, cities bear a significant responsibility to uphold environmental protection measures …

Concept drift is a phenomenon where the distribution of data streams changes over time.
When this happens, model predictions become less accurate. Hence, models built in the …

A growing number of applications generate streaming data, making data stream mining a
popular research topic. Classification-based streaming algorithms require pre-training on …

Abstract Concept drift poses significant challenges in the fields of machine learning and data
mining. At present, many existing algorithms struggle to maintain low error rates or require …

Industrial data streams frequently experience concept drifts. Current drift detection methods,
focusing on prediction performance or data distribution, often neglect temporal …

The existence of concept drift phenomenon seriously affects the quality of data, it is an
urgent need to investigate accurate concept drift detection methods to improve the data …

Dynamic streaming data is widespread in various real-world scenarios, and the distribution
may change under unforeseen disturbances. The decrease in predicted performance …

As time passes, the data within information systems may continuously evolve, causing the
target concept to drift. To ensure the effectiveness of data-driven decision making, it is …