How good is the Electricity benchmark for evaluating concept drift adaptation
Not on the map. Why?
Abstract (arXiv)
In this correspondence, we will point out a problem with testing adaptive classifiers on autocorrelated data. In such a case random change alarms may boost the accuracy figures. Hence, we cannot be sure if the adaptation is working well.
Key facts. Cited by 8 papers in the map (data through 14 Oct 2026). First posted on arXiv 15 Jan 2013.
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Citing papers per year: 2021: 1; 2022: 2; 2023: 0; 2024: 4; 2025: 0; 2026: 1.
8 papers.
- 2022Data-SUITE: Data-centric identification of in-distribution incongruous examplescited by 12
- 2024Self-Healing Machine Learning: A Framework for Autonomous Adaptation in Real-World Environmentscited by 8
- 2022Learning from Data Streams: An Overview and Updatecited by 5
- 2024A Scalable Approach to Covariate and Concept Drift Management via Adaptive Data Segmentationcited by 2
- 2024A Comprehensive Review of Machine Learning Advances on Data Change: A Cross-Field Perspectivecited by 1
- 2024An Efficient Model Maintenance Approach for MLOpscited by 1
- 2026The Window Dilemma: Why Concept Drift Detection is Ill-Posedcited by 1
- 2021Sketches for Time-Dependent Machine Learningcited by 0
- 2026The Window Dilemma: Why Concept Drift Detection is Ill-Posed6 Feb 2026
- 2024An Efficient Model Maintenance Approach for MLOps5 Dec 2024
- 2024A Scalable Approach to Covariate and Concept Drift Management via Adaptive Data Segmentation23 Nov 2024
- 2024Self-Healing Machine Learning: A Framework for Autonomous Adaptation in Real-World Environments31 Oct 2024
- 2024A Comprehensive Review of Machine Learning Advances on Data Change: A Cross-Field Perspective20 Feb 2024
- 2022Learning from Data Streams: An Overview and Update30 Dec 2022
- 2022Data-SUITE: Data-centric identification of in-distribution incongruous examples17 Feb 2022
- 2021Sketches for Time-Dependent Machine Learning26 Aug 2021