# Concept Drift Detection for Streaming Data

arXiv: https://arxiv.org/abs/1504.01044 · HTML: https://mlsanity.com/paper/1504.01044/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Apr 2015.

## Built on by (most cited first, 18 of 18)

- [From Concept Drift to Model Degradation: An Overview on Performance-Aware Drift Detectors](https://mlsanity.com/paper/2203.11070/) (2022)
- [Sintel: A Machine Learning Framework to Extract Insights from Signals](https://mlsanity.com/paper/2204.09108/) (2022)
- [SoK: Machine Learning Governance](https://mlsanity.com/paper/2109.10870/) (2021)
- [Self-Healing Machine Learning: A Framework for Autonomous Adaptation in Real-World Environments](https://mlsanity.com/paper/2411.00186/) (2024)
- [In-context Learning of Evolving Data Streams with Tabular Foundational Models](https://mlsanity.com/paper/2502.16840/) (2025)
- [Digital Twin-based Anomaly Detection with Curriculum Learning in Cyber-physical Systems](https://mlsanity.com/paper/2309.15995/) (2023)
- [Unified Shapley Framework to Explain Prediction Drift](https://mlsanity.com/paper/2102.07862/) (2021)
- [Customs Fraud Detection in the Presence of Concept Drift](https://mlsanity.com/paper/2109.14155/) (2021)
- [OEBench: Investigating Open Environment Challenges in Real-World Relational Data Streams](https://mlsanity.com/paper/2308.15059/) (2023)
- [SHIFT15M: Fashion-specific dataset for set-to-set matching with several distribution shifts](https://mlsanity.com/paper/2108.12992/) (2021)
- [Towards an MLOps Architecture for XAI in Industrial Applications](https://mlsanity.com/paper/2309.12756/) (2023)
- [Adaptive Hoeffding Tree with Transfer Learning for Streaming Synchrophasor Data Sets](https://mlsanity.com/paper/2501.16354/) (2025)
- [Frouros: A Python library for drift detection in machine learning systems](https://mlsanity.com/paper/2208.06868/) (2022)
- [SMOClust: Synthetic Minority Oversampling based on Stream Clustering for Evolving Data Streams](https://mlsanity.com/paper/2308.14845/) (2023)
- [Data Quality Aware Approaches for Addressing Model Drift of Semantic Segmentation Models](https://mlsanity.com/paper/2402.07258/) (2024)
- [Fairness Hub Technical Briefs: Definition and Detection of Distribution Shift](https://mlsanity.com/paper/2405.14186/) (2024)
- [A Hybrid Framework for Real-Time Data Drift and Anomaly Identification Using Hierarchical Temporal Memory and Statistical Tests](https://mlsanity.com/paper/2504.18599/) (2025)
- [Flexible and Efficient Drift Detection without Labels](https://mlsanity.com/paper/2506.08734/) (2025)

## Abstract (arXiv)

Common statistical prediction models often require and assume stationarity in the data. However, in many practical applications, changes in the relationship of the response and predictor variables are regularly observed over time, resulting in the deterioration of the predictive performance of these models. This paper presents Linear Four Rates (LFR), a framework for detecting these concept drifts and subsequently identifying the data points that belong to the new concept (for relearning the model). Unlike conventional concept drift detection approaches, LFR can be applied to both batch and stream data; is not limited by the distribution properties of the response variable (e.g., datasets with imbalanced labels); is independent of the underlying statistical-model; and uses user-specified parameters that are intuitively comprehensible. The performance of LFR is compared to benchmark approaches using both simulated and commonly used public datasets that span the gamut of concept drift types. The results show LFR significantly outperforms benchmark approaches in terms of recall, accuracy and delay in detection of concept drifts across datasets.