# From Concept Drift to Model Degradation: An Overview on Performance-Aware Drift Detectors

arXiv: https://arxiv.org/abs/2203.11070 · HTML: https://mlsanity.com/paper/2203.11070/

## Key facts

- Cited by 83 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 197 references that are in the map.
- First posted on arXiv 21 Mar 2022.

## Builds on (most cited first, 22 of 22)

- [Learning under Concept Drift: A Review](https://mlsanity.com/paper/2004.05785/) (2020)
- [Detecting and Correcting for Label Shift with Black Box Predictors](https://mlsanity.com/paper/1802.03916/) (2018)
- [Concept Whitening for Interpretable Image Recognition](https://mlsanity.com/paper/2002.01650/) (2020)
- [Deep Learning: A Critical Appraisal](https://mlsanity.com/paper/1801.00631/) (2018)
- [Regularized Learning for Domain Adaptation under Label Shifts](https://mlsanity.com/paper/1903.09734/) (2019)
- [Characterizing Concept Drift](https://mlsanity.com/paper/1511.03816/) (2015)
- [Analysis of Explainers of Black Box Deep Neural Networks for Computer Vision: A Survey](https://mlsanity.com/paper/1911.12116/) (2019)
- [Knowledge-Preserving Incremental Social Event Detection via Heterogeneous GNNs](https://mlsanity.com/paper/2101.08747/) (2021)
- [Spiking Neural Networks and Online Learning: An Overview and Perspectives](https://mlsanity.com/paper/1908.08019/) (2019)
- [A Systematic Study of Online Class Imbalance Learning with Concept Drift](https://mlsanity.com/paper/1703.06683/) (2017)
- [Concept Drift Detection for Streaming Data](https://mlsanity.com/paper/1504.01044/) (2015)
- [Concept Drift and Anomaly Detection in Graph Streams](https://mlsanity.com/paper/1706.06941/) (2017)
- [McDiarmid Drift Detection Methods for Evolving Data Streams](https://mlsanity.com/paper/1710.02030/) (2017)
- [Adaptive Online Incremental Learning for Evolving Data Streams](https://mlsanity.com/paper/2201.01633/) (2022)
- [Reservoir of Diverse Adaptive Learners and Stacking Fast Hoeffding Drift Detection Methods for Evolving Data Streams](https://mlsanity.com/paper/1709.02457/) (2017)
- [Positive-Unlabeled Classification under Class Prior Shift and Asymmetric Error](https://mlsanity.com/paper/1809.07011/) (2018)
- [Concept Drift Detection and Adaptation with Hierarchical Hypothesis Testing](https://mlsanity.com/paper/1707.07821/) (2017)
- [Recurring Concept Meta-learning for Evolving Data Streams](https://mlsanity.com/paper/1905.08848/) (2019)
- [Handling Adversarial Concept Drift in Streaming Data](https://mlsanity.com/paper/1803.09160/) (2018)
- [Shift Happens: Adjusting Classifiers](https://mlsanity.com/paper/2111.02529/) (2021)
- [Concept Drift Learning with Alternating Learners](https://mlsanity.com/paper/1710.06940/) (2017)
- [LUNAR: Cellular Automata for Drifting Data Streams](https://mlsanity.com/paper/2002.02164/) (2020)

## Built on by (most cited first, 30 of 83)

- [Continual Learning in Medical Image Analysis: A Comprehensive Review of Recent Advancements and Future Prospects](https://mlsanity.com/paper/2312.17004/) (2023)
- [AI Risk Management Should Incorporate Both Safety and Security](https://mlsanity.com/paper/2405.19524/) (2024)
- [Unsupervised Concept Drift Detection from Deep Learning Representations in Real-time](https://mlsanity.com/paper/2406.17813/) (2024)
- [DC-Check: A Data-Centric AI checklist to guide the development of reliable machine learning systems](https://mlsanity.com/paper/2211.05764/) (2022)
- [Online Drift Detection with Maximum Concept Discrepancy](https://mlsanity.com/paper/2407.05375/) (2024)
- [Generalization in Neural Networks: A Broad Survey](https://mlsanity.com/paper/2209.01610/) (2022)
- [Sequential Harmful Shift Detection Without Labels](https://mlsanity.com/paper/2412.12910/) (2024)
- [An Auditing Test To Detect Behavioral Shift in Language Models](https://mlsanity.com/paper/2410.19406/) (2024)
- [DA-LSTM: A Dynamic Drift-Adaptive Learning Framework for Interval Load Forecasting with LSTM Networks](https://mlsanity.com/paper/2305.08767/) (2023)
- [Confidence-based Estimators for Predictive Performance in Model Monitoring](https://mlsanity.com/paper/2407.08649/) (2024)
- [Towards flexible perception with visual memory](https://mlsanity.com/paper/2408.08172/) (2024)
- [Self-Healing Machine Learning: A Framework for Autonomous Adaptation in Real-World Environments](https://mlsanity.com/paper/2411.00186/) (2024)
- [Are Concept Drift Detectors Reliable Alarming Systems? -- A Comparative Study](https://mlsanity.com/paper/2211.13098/) (2022)
- [How Object Information Improves Skeleton-based Human Action Recognition in Assembly Tasks](https://mlsanity.com/paper/2306.05844/) (2023)
- [Efficient Concept Drift Handling for Batch Android Malware Detection Models](https://mlsanity.com/paper/2309.09807/) (2023)
- [DSAP: Analyzing Bias Through Demographic Comparison of Datasets](https://mlsanity.com/paper/2312.14626/) (2023)
- [AI-driven Wireless Positioning: Fundamentals, Standards, State-of-the-art, and Challenges](https://mlsanity.com/paper/2501.14970/) (2025)
- [Deep Hierarchical Learning with Nested Subspace Networks for Large Language Models](https://mlsanity.com/paper/2509.17874/) (2025)
- [An investigation of challenges encountered when specifying training data and runtime monitors for safety critical ML applications](https://mlsanity.com/paper/2301.13476/) (2023)
- [Test & Evaluation Best Practices for Machine Learning-Enabled Systems](https://mlsanity.com/paper/2310.06800/) (2023)
- [Towards Trustworthy Machine Learning in Production: An Overview of the Robustness in MLOps Approach](https://mlsanity.com/paper/2410.21346/) (2024)
- [DriftGuard: Safety-Aware Multi-Monitor Detection and Selective Adaptation for Evolving Toxicity Moderation](https://mlsanity.com/paper/2606.28725/) (2026)
- [Is Your Anomaly Detector Ready for Change? Adapting AIOps Solutions to the Real World](https://mlsanity.com/paper/2311.10421/) (2023)
- [Open-Source Drift Detection Tools in Action: Insights from Two Use Cases](https://mlsanity.com/paper/2404.18673/) (2024)
- [IncA-DES: An incremental and adaptive dynamic ensemble selection approach using online K-d tree neighborhood search for data streams with concept drift](https://mlsanity.com/paper/2507.12573/) (2025)
- [MLHOps: Machine Learning for Healthcare Operations](https://mlsanity.com/paper/2305.02474/) (2023)
- [5G Positioning Advancements with AI/ML](https://mlsanity.com/paper/2401.02427/) (2023)
- [Operational range bounding of spectroscopy models with anomaly detection](https://mlsanity.com/paper/2408.02581/) (2024)
- [AutoML for Multi-Class Anomaly Compensation of Sensor Drift](https://mlsanity.com/paper/2502.19180/) (2025)
- [Understanding Concept Drift with Deprecated Permissions in Android Malware Detection](https://mlsanity.com/paper/2507.22231/) (2025)

## Abstract (arXiv)

The dynamicity of real-world systems poses a significant challenge to deployed predictive machine learning (ML) models. Changes in the system on which the ML model has been trained may lead to performance degradation during the system's life cycle. Recent advances that study non-stationary environments have mainly focused on identifying and addressing such changes caused by a phenomenon called concept drift. Different terms have been used in the literature to refer to the same type of concept drift and the same term for various types. This lack of unified terminology is set out to create confusion on distinguishing between different concept drift variants. In this paper, we start by grouping concept drift types by their mathematical definitions and survey the different terms used in the literature to build a consolidated taxonomy of the field. We also review and classify performance-based concept drift detection methods proposed in the last decade. These methods utilize the predictive model's performance degradation to signal substantial changes in the systems. The classification is outlined in a hierarchical diagram to provide an orderly navigation between the methods. We present a comprehensive analysis of the main attributes and strategies for tracking and evaluating the model's performance in the predictive system. The paper concludes by discussing open research challenges and possible research directions.