# A comprehensive analysis of concept drift locality in data streams

arXiv: https://arxiv.org/abs/2311.06396 · HTML: https://mlsanity.com/paper/2311.06396/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 63 references that are in the map.
- First posted on arXiv 10 Nov 2023.

## Builds on (most cited first, 10 of 10)

- [Learning under Concept Drift: A Review](https://mlsanity.com/paper/2004.05785/) (2020)
- [River: machine learning for streaming data in Python](https://mlsanity.com/paper/2012.04740/) (2020)
- [Characterizing Concept Drift](https://mlsanity.com/paper/1511.03816/) (2015)
- [Challenges in Benchmarking Stream Learning Algorithms with Real-world Data](https://mlsanity.com/paper/2005.00113/) (2020)
- [Reactive Soft Prototype Computing for Concept Drift Streams](https://mlsanity.com/paper/2007.05432/) (2020)
- [A survey on learning from imbalanced data streams: taxonomy, challenges, empirical study, and reproducible experimental framework](https://mlsanity.com/paper/2204.03719/) (2022)
- [STUDD: A Student-Teacher Method for Unsupervised Concept Drift Detection](https://mlsanity.com/paper/2103.00903/) (2021)
- [Concept Drift Detection from Multi-Class Imbalanced Data Streams](https://mlsanity.com/paper/2104.10228/) (2021)
- [Reservoir of Diverse Adaptive Learners and Stacking Fast Hoeffding Drift Detection Methods for Evolving Data Streams](https://mlsanity.com/paper/1709.02457/) (2017)
- [Are Concept Drift Detectors Reliable Alarming Systems? -- A Comparative Study](https://mlsanity.com/paper/2211.13098/) (2022)

## Built on by (most cited first, 10 of 10)

- [Online Drift Detection with Maximum Concept Discrepancy](https://mlsanity.com/paper/2407.05375/) (2024)
- [Modyn: Data-Centric Machine Learning Pipeline Orchestration](https://mlsanity.com/paper/2312.06254/) (2023)
- [Hoeffding adaptive trees for multi-label classification on data streams](https://mlsanity.com/paper/2410.20242/) (2024)
- [How well does Classification Accuracy capture Concept Drift Detection Quality? An overview of Concept Drift Detection evaluation](https://mlsanity.com/paper/2605.31186/) (2026)
- [Drift Happens: An Empirical Study of Neural Architecture Robustness to Temporal Distribution Shift](https://mlsanity.com/paper/2607.05908/) (2026)
- [TRACE: A Multi-Layer Benchmark for Human AI Controller Coordination Under Drift and Failure](https://mlsanity.com/paper/2608.06657/) (2026)
- [Hoeffding adaptive splitting trees for data stream classification with concept drift and ensemble learning](https://mlsanity.com/paper/2608.16659/) (2026)
- [Provenance Guided Incremental Learning Under Evolving Concept Definitions](https://mlsanity.com/paper/2608.23893/) (2026)
- [Candidate Comparability Before Promotion: Conditional Validation in Adaptive Network Intrusion Detection](https://mlsanity.com/paper/2609.04388/) (2026)
- [SCCM : Stream Cruise Control Method for Automated Drift Detection and Adaptation](https://mlsanity.com/paper/2609.09432/) (2026)

## Abstract (arXiv)

Adapting to drifting data streams is a significant challenge in online learning. Concept drift must be detected for effective model adaptation to evolving data properties. Concept drift can impact the data distribution entirely or partially, which makes it difficult for drift detectors to accurately identify the concept drift. Despite the numerous concept drift detectors in the literature, standardized procedures and benchmarks for comprehensive evaluation considering the locality of the drift are lacking. We present a novel categorization of concept drift based on its locality and scale. A systematic approach leads to a set of 2,760 benchmark problems, reflecting various difficulty levels following our proposed categorization. We conduct a comparative assessment of 9 state-of-the-art drift detectors across diverse difficulties, highlighting their strengths and weaknesses for future research. We examine how drift locality influences the classifier performance and propose strategies for different drift categories to minimize the recovery time. Lastly, we provide lessons learned and recommendations for future concept drift research. Our benchmark data streams and experiments are publicly available at https://github.com/gabrieljaguiar/locality-concept-drift.