# One or Two Things We know about Concept Drift -- A Survey on Monitoring Evolving Environments

arXiv: https://arxiv.org/abs/2310.15826 · HTML: https://mlsanity.com/paper/2310.15826/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 93 references that are in the map.
- First posted on arXiv 24 Oct 2023.

## Builds on (most cited first, 17 of 17)

- ["Why Should I Trust You?": Explaining the Predictions of Any Classifier](https://mlsanity.com/paper/1602.04938/) (2016)
- [Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps](https://mlsanity.com/paper/1312.6034/) (2013)
- [Learning under Concept Drift: A Review](https://mlsanity.com/paper/2004.05785/) (2020)
- [Failing Loudly: An Empirical Study of Methods for Detecting Dataset Shift](https://mlsanity.com/paper/1810.11953/) (2018)
- [Techniques for Interpretable Machine Learning](https://mlsanity.com/paper/1808.00033/) (2018)
- [Interpretable Counterfactual Explanations Guided by Prototypes](https://mlsanity.com/paper/1907.02584/) (2019)
- [Reconstructing Training Data from Trained Neural Networks](https://mlsanity.com/paper/2206.07758/) (2022)
- [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)
- [Scikit-Multiflow: A Multi-output Streaming Framework](https://mlsanity.com/paper/1807.04662/) (2018)
- [Reactive Soft Prototype Computing for Concept Drift Streams](https://mlsanity.com/paper/2007.05432/) (2020)
- [Converting High-Dimensional Regression to High-Dimensional Conditional Density Estimation](https://mlsanity.com/paper/1704.08095/) (2017)
- [Model Based Explanations of Concept Drift](https://mlsanity.com/paper/2303.09331/) (2023)
- [Diagnosing Concept Drift with Visual Analytics](https://mlsanity.com/paper/2007.14372/) (2020)
- [ConceptExplorer: Visual Analysis of Concept Driftsin Multi-source Time-series Data](https://mlsanity.com/paper/2007.15272/) (2020)
- [Understanding Concept Drift](https://mlsanity.com/paper/1704.00362/) (2017)
- [Suitability of Different Metric Choices for Concept Drift Detection](https://mlsanity.com/paper/2202.09486/) (2022)

## Built on by (most cited first, 7 of 7)

- [Investigating the Suitability of Concept Drift Detection for Detecting Leakages in Water Distribution Networks](https://mlsanity.com/paper/2401.01733/) (2024)
- [Learner-based Concept Drift Detection: Analysis and Evaluation](https://mlsanity.com/paper/2606.20216/) (2026)
- [A Remark on Concept Drift for Dependent Data](https://mlsanity.com/paper/2312.10212/) (2023)
- [Kernel conditional tests from learning-theoretic bounds](https://mlsanity.com/paper/2506.03898/) (2025)
- [Hybrid Neuro-Symbolic Models for Ethical AI in Risk-Sensitive Domains](https://mlsanity.com/paper/2511.17644/) (2025)
- [Robust Outlier Detection and Low-Latency Concept Drift Adaptation for Data Stream Regression: A Dual-Channel Architecture](https://mlsanity.com/paper/2512.12289/) (2025)
- [ShapShift: Explaining Model Prediction Shifts with Subgroup Conditional Shapley Values](https://mlsanity.com/paper/2604.11200/) (2026)

## Abstract (arXiv)

The world surrounding us is subject to constant change. These changes, frequently described as concept drift, influence many industrial and technical processes. As they can lead to malfunctions and other anomalous behavior, which may be safety-critical in many scenarios, detecting and analyzing concept drift is crucial. In this paper, we provide a literature review focusing on concept drift in unsupervised data streams. While many surveys focus on supervised data streams, so far, there is no work reviewing the unsupervised setting. However, this setting is of particular relevance for monitoring and anomaly detection which are directly applicable to many tasks and challenges in engineering. This survey provides a taxonomy of existing work on drift detection. Besides, it covers the current state of research on drift localization in a systematic way. In addition to providing a systematic literature review, this work provides precise mathematical definitions of the considered problems and contains standardized experiments on parametric artificial datasets allowing for a direct comparison of different strategies for detection and localization. Thereby, the suitability of different schemes can be analyzed systematically and guidelines for their usage in real-world scenarios can be provided. Finally, there is a section on the emerging topic of explaining concept drift.