# Learning Parameter Distributions to Detect Concept Drift in Data Streams

arXiv: https://arxiv.org/abs/2010.09388 · HTML: https://mlsanity.com/paper/2010.09388/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Oct 2020.

## Built on by (most cited first, 12 of 12)

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- [A Survey of Machine Unlearning](https://mlsanity.com/paper/2209.02299/) (2022)
- [On Baselines for Local Feature Attributions](https://mlsanity.com/paper/2101.00905/) (2021)
- [Change Detection for Local Explainability in Evolving Data Streams](https://mlsanity.com/paper/2209.02764/) (2022)
- [DA-LSTM: A Dynamic Drift-Adaptive Learning Framework for Interval Load Forecasting with LSTM Networks](https://mlsanity.com/paper/2305.08767/) (2023)
- [Dynamic Model Tree for Interpretable Data Stream Learning](https://mlsanity.com/paper/2203.16181/) (2022)
- [Learning from Data Streams: An Overview and Update](https://mlsanity.com/paper/2212.14720/) (2022)
- [Standardized Evaluation of Machine Learning Methods for Evolving Data Streams](https://mlsanity.com/paper/2204.13625/) (2022)
- [Unsupervised Detection of Behavioural Drifts with Dynamic Clustering and Trajectory Analysis](https://mlsanity.com/paper/2302.06228/) (2023)
- [Employing Two-Dimensional Word Embedding for Difficult Tabular Data Stream Classification](https://mlsanity.com/paper/2404.15836/) (2024)
- [Adapting to Change: Robust Counterfactual Explanations in Dynamic Data Landscapes](https://mlsanity.com/paper/2308.02353/) (2023)
- [Online Feature Selection for Efficient Learning in Networked Systems](https://mlsanity.com/paper/2112.08253/) (2021)

## Abstract (arXiv)

Data distributions in streaming environments are usually not stationary. In order to maintain a high predictive quality at all times, online learning models need to adapt to distributional changes, which are known as concept drift. The timely and robust identification of concept drift can be difficult, as we never have access to the true distribution of streaming data. In this work, we propose a novel framework for the detection of real concept drift, called ERICS. By treating the parameters of a predictive model as random variables, we show that concept drift corresponds to a change in the distribution of optimal parameters. To this end, we adopt common measures from information theory. The proposed framework is completely model-agnostic. By choosing an appropriate base model, ERICS is also capable to detect concept drift at the input level, which is a significant advantage over existing approaches. An evaluation on several synthetic and real-world data sets suggests that the proposed framework identifies concept drift more effectively and precisely than various existing works.