Work

Conceptualizing Concept Drift

Year: 2025

Type: conference-paper

Abstract: Concept drift refers to the phenomenon that the underlying data distribution changes over time.While detection methods or model adjustment methods exist, a proper explanation of drift in high-dimensional settings is still widely unsolved.This problem is crucial since it enables an understanding of the most prominent drift characteristics.In this work, we propose to explain concept drift of high-dimensional data objects by means of concept activation vectors which give rise to local, phase, and a novel, global explanation called the Concept 2 Drift Distribution.

Source: European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN)

DOI: 10.14428/esann/2025.es2025-117

Authors: Isaac Roberts, Fabian Hinder, Valerie Vaquet, Alexander Schulz, Barbara Hammer

Institution: Bielefeld University

Language: English

Topic: Data Stream Mining Techniques

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: bronze

Funders: European Commission, Bundesministerium für Bildung und Forschung, HORIZON EUROPE Framework Programme, HORIZON EUROPE Marie Sklodowska-Curie Actions

Award: Learning with Multiple Representations