# How well does Classification Accuracy capture Concept Drift Detection Quality? An overview of Concept Drift Detection evaluation

arXiv: https://arxiv.org/abs/2605.31186 · HTML: https://mlsanity.com/paper/2605.31186/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 32 references that are in the map.
- First posted on arXiv 29 May 2026.

## Builds on (most cited first, 7 of 7)

- [Learning under Concept Drift: A Review](https://mlsanity.com/paper/2004.05785/) (2020)
- [Scikit-Multiflow: A Multi-output Streaming Framework](https://mlsanity.com/paper/1807.04662/) (2018)
- [Challenges in Benchmarking Stream Learning Algorithms with Real-world Data](https://mlsanity.com/paper/2005.00113/) (2020)
- [A Survey on Semi-Supervised Learning for Delayed Partially Labelled Data Streams](https://mlsanity.com/paper/2106.09170/) (2021)
- [stream-learn -- open-source Python library for difficult data stream batch analysis](https://mlsanity.com/paper/2001.11077/) (2020)
- [A comprehensive analysis of concept drift locality in data streams](https://mlsanity.com/paper/2311.06396/) (2023)
- [Synthetic Non-stationary Data Streams for Recognition of the Unknown](https://mlsanity.com/paper/2505.13745/) (2025)

## Abstract (arXiv)

Data streams are nowadays among the most frequently analyzed data structures, with the concept drift posing a major challenge encountered by processing systems. Despite the proposition of numerous solutions to counteract the accuracy degeneration due to concept drift, the scientific community has not yet established a unified framework for evaluating the concept drift detection task. Existing research often relies on classification quality metrics, but these can be affected by multiple factors and may not reliably reflect drift detection quality. In this work, we present an in-depth overview of the relationship between metrics for quantifying drift detection quality and classification performance in synthetic nonstationary data streams. The proposed research studies eight drift detection quality metrics in relation to the classifier's performance across seven synthetic data stream generation tools, additionally considering drift dynamics as a factor. The studies aim to identify the most informative set of drift detection quality metrics and provide a deep understanding of the method's evaluation.