English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Concept drift detection has attracted considerable attention due to itsimportance in many real-world applications such as health monitoring and faultdiagnosis. Conventionally, most advanced approaches will be of poor performancewhen the evaluation criteria of the environment has changed (i.e. conceptdrift), either can only detect and adapt to virtual drift. In this paper, wepropose a new approach to detect real-drift in the chunk data stream withlimited annotations based on concept confusion. When a new data chunk arrives,we use both real labels and pseudo labels to update the model after predictionand drift detection. In this context, the model will be confused and yieldsprediction difference once drift occurs. We then adopt cosine similarity tomeasure the difference. And an adaptive threshold method is proposed to findthe abnormal value. Experiments show that our method has a low false alarm rateand false negative rate with the utilization of different classifiers.

CADM: Confusion Model-based Detection Method for Real-drift in Chunk Data Stream