Configuration of the data streams (A: Abrupt Drift, G: Gradual

Download scientific diagram | Configuration of the data streams (A: Abrupt Drift, G: Gradual Drift, I m : Moderate Incremental Drift, I f : Fast Incremental Drift and N: No Drift) from publication: Passive concept drift handling via variations of learning vector quantization | Concept drift is a change of the underlying data distribution which occurs especially with streaming data. Besides other challenges in the field of streaming data classification, concept drift has to be addressed to obtain reliable predictions. Robust Soft Learning Vector | Concept Drift, Quantization and Vectorization | ResearchGate, the professional network for scientists.

Adapting to Change: The Essential Guide to Drift Detection and

Classification accuracy percentages calculated for the RCV1-v2 dataset

Future Internet, Free Full-Text

Disposition-Based Concept Drift Detection and Adaptation in Data

Illustration of main idea: our approach periodically conducts the model

Applied Sciences, Free Full-Text

A Novel Framework for Concept Drift Detection using Autoencoders

Snapshots of sudden drifting Hyperplane, illustrating concept mean

The accumulate accuracy on RTG1 dataset when the domain similarity is 0.25