Learning Unbiased Cluster Descriptors for Interpretable Imbalanced Concept Drift Detection

ArXi:2603.06757v1 Announce Type: new Unlabeled streaming data are usually collected to describe dynamic systems, where concept drift detection is a vital prerequisite to understanding the evolution of systems. However, the drifting concepts are usually imbalanced in most real cases, which brings great challenges to drift detection. That is, the dominant statistics of large clusters can easily mask the drifting of small cluster distributions (also called small concepts), which is known as the `masking effect