# On the Reliable Detection of Concept Drift from Streaming Unlabeled Data

arXiv: https://arxiv.org/abs/1704.00023 · HTML: https://mlsanity.com/paper/1704.00023/

## Key facts

- Cited by 33 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Mar 2017.

## Built on by (most cited first, 30 of 33)

- [Gradual Domain Adaptation without Indexed Intermediate Domains](https://mlsanity.com/paper/2207.04587/) (2022)
- [Continual Learning for Predictive Maintenance: Overview and Challenges](https://mlsanity.com/paper/2301.12467/) (2023)
- [Change Detection for Local Explainability in Evolving Data Streams](https://mlsanity.com/paper/2209.02764/) (2022)
- [Test for non-negligible adverse shifts](https://mlsanity.com/paper/2107.02990/) (2021)
- [Standardized Evaluation of Machine Learning Methods for Evolving Data Streams](https://mlsanity.com/paper/2204.13625/) (2022)
- [Task-Sensitive Concept Drift Detector with Constraint Embedding](https://mlsanity.com/paper/2108.06980/) (2021)
- [A Broad Ensemble Learning System for Drifting Stream Classification](https://mlsanity.com/paper/2110.03540/) (2021)
- [Unsupervised Detection of Behavioural Drifts with Dynamic Clustering and Trajectory Analysis](https://mlsanity.com/paper/2302.06228/) (2023)
- [How to Sustainably Monitor ML-Enabled Systems? Accuracy and Energy Efficiency Tradeoffs in Concept Drift Detection](https://mlsanity.com/paper/2404.19452/) (2024)
- [An Efficient Model-Agnostic Approach for Uncertainty Estimation in Data-Restricted Pedometric Applications](https://mlsanity.com/paper/2409.11985/) (2024)
- [TensAIR: Real-Time Training of Neural Networks from Data-streams](https://mlsanity.com/paper/2211.10280/) (2022)
- [Towards Computational Performance Engineering for Unsupervised Concept Drift Detection -- Complexities, Benchmarking, Performance Analysis](https://mlsanity.com/paper/2304.08319/) (2023)
- [MLHOps: Machine Learning for Healthcare Operations](https://mlsanity.com/paper/2305.02474/) (2023)
- [Reliable and Interpretable Drift Detection in Streams of Short Texts](https://mlsanity.com/paper/2305.17750/) (2023)
- [Minimax Forward and Backward Learning of Evolving Tasks with Performance Guarantees](https://mlsanity.com/paper/2310.15974/) (2023)
- [Learning Unbiased Cluster Descriptors for Interpretable Imbalanced Concept Drift Detection](https://mlsanity.com/paper/2603.06757/) (2026)
- [Detecting Concept Drift in the Presence of Sparsity -- A Case Study of Automated Change Risk Assessment System](https://mlsanity.com/paper/2207.13287/) (2022)
- [Frouros: A Python library for drift detection in machine learning systems](https://mlsanity.com/paper/2208.06868/) (2022)
- [Time-Aware Datasets are Adaptive Knowledgebases for the New Normal](https://mlsanity.com/paper/2211.12508/) (2022)
- [Adapting to Change: Robust Counterfactual Explanations in Dynamic Data Landscapes](https://mlsanity.com/paper/2308.02353/) (2023)
- [Gradient Boosting Mapping for Dimensionality Reduction and Feature Extraction](https://mlsanity.com/paper/2405.08486/) (2024)
- [Implicit Concept Drift Detection for Multi-label Data Streams](https://mlsanity.com/paper/2202.00070/) (2022)
- [Dynamic Template Selection Through Change Detection for Adaptive Siamese Tracking](https://mlsanity.com/paper/2203.03181/) (2022)
- [Forgetful Forests: high performance learning data structures for streaming data under concept drift](https://mlsanity.com/paper/2212.07876/) (2022)
- [Towards Practicable Sequential Shift Detectors](https://mlsanity.com/paper/2307.14758/) (2023)
- [Expert-Driven Monitoring of Operational ML Models](https://mlsanity.com/paper/2401.11993/) (2024)
- [Drift Detection: Introducing Gaussian Split Detector](https://mlsanity.com/paper/2405.08637/) (2024)
- [Time to Retrain? Detecting Concept Drifts in Machine Learning Systems](https://mlsanity.com/paper/2410.09190/) (2024)
- [SUDS: A Strategy for Unsupervised Drift Sampling](https://mlsanity.com/paper/2411.02995/) (2024)
- [Flexible and Efficient Drift Detection without Labels](https://mlsanity.com/paper/2506.08734/) (2025)

## Abstract (arXiv)

Classifiers deployed in the real world operate in a dynamic environment, where the data distribution can change over time. These changes, referred to as concept drift, can cause the predictive performance of the classifier to drop over time, thereby making it obsolete. To be of any real use, these classifiers need to detect drifts and be able to adapt to them, over time. Detecting drifts has traditionally been approached as a supervised task, with labeled data constantly being used for validating the learned model. Although effective in detecting drifts, these techniques are impractical, as labeling is a difficult, costly and time consuming activity. On the other hand, unsupervised change detection techniques are unreliable, as they produce a large number of false alarms. The inefficacy of the unsupervised techniques stems from the exclusion of the characteristics of the learned classifier, from the detection process. In this paper, we propose the Margin Density Drift Detection (MD3) algorithm, which tracks the number of samples in the uncertainty region of a classifier, as a metric to detect drift. The MD3 algorithm is a distribution independent, application independent, model independent, unsupervised and incremental algorithm for reliably detecting drifts from data streams. Experimental evaluation on 6 drift induced datasets and 4 additional datasets from the cybersecurity domain demonstrates that the MD3 approach can reliably detect drifts, with significantly fewer false alarms compared to unsupervised feature based drift detectors. The reduced false alarms enables the signaling of drifts only when they are most likely to affect classification performance. As such, the MD3 approach leads to a detection scheme which is credible, label efficient and general in its applicability.