Fingerprinting Concepts in Data Streams with Supervised and Unsupervised Meta-Information

ArXi:2603.11094v1 Announce Type: new Streaming sources of data are becoming common as the ability to collect data in real-time grows. A major concern in dealing with data streams is concept drift, a change in the distribution of data over time, for example, due to changes in environmental conditions. Representing concepts (stationary periods featuring similar behaviour) is a key idea in adapting to concept drift. By testing the similarity of a concept representation to a window of observations, we can detect concept drift to a new or previously seen recurring concept.