# Reactive Soft Prototype Computing for Concept Drift Streams

arXiv: https://arxiv.org/abs/2007.05432 · HTML: https://mlsanity.com/paper/2007.05432/

## Key facts

- Cited by 40 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Jul 2020.

## Built on by (most cited first, 30 of 40)

- [Detecting Concept Drift With Neural Network Model Uncertainty](https://mlsanity.com/paper/2107.01873/) (2021)
- [Online Drift Detection with Maximum Concept Discrepancy](https://mlsanity.com/paper/2407.05375/) (2024)
- [A comprehensive analysis of concept drift locality in data streams](https://mlsanity.com/paper/2311.06396/) (2023)
- [LEAF: Navigating Concept Drift in Cellular Networks](https://mlsanity.com/paper/2109.03011/) (2021)
- [Early Concept Drift Detection via Prediction Uncertainty](https://mlsanity.com/paper/2412.11158/) (2024)
- [When to retrain a machine learning model](https://mlsanity.com/paper/2505.14903/) (2025)
- [Cost-Effective Retraining of Machine Learning Models](https://mlsanity.com/paper/2310.04216/) (2023)
- [One or Two Things We know about Concept Drift -- A Survey on Monitoring Evolving Environments](https://mlsanity.com/paper/2310.15826/) (2023)
- [Fast & Furious: Modelling Malware Detection as Evolving Data Streams](https://mlsanity.com/paper/2205.12311/) (2022)
- [Autoregressive based Drift Detection Method](https://mlsanity.com/paper/2203.04769/) (2022)
- [Identifying Predictions That Influence the Future: Detecting Performative Concept Drift in Data Streams](https://mlsanity.com/paper/2412.10545/) (2024)
- [FLAME: Adaptive and Reactive Concept Drift Mitigation for Federated Learning Deployments](https://mlsanity.com/paper/2410.01386/) (2024)
- [TRACE: A Generalizable Drift Detector for Streaming Data-Driven Optimization](https://mlsanity.com/paper/2512.07082/) (2025)
- [Online Shift Detection and Conformal Adaptation for Deployed Safety Classifiers](https://mlsanity.com/paper/2606.11949/) (2026)
- [Domain Specific Concept Drift Detectors for Predicting Financial Time Series](https://mlsanity.com/paper/2103.14079/) (2021)
- [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)
- [LE3D: A Lightweight Ensemble Framework of Data Drift Detectors for Resource-Constrained Devices](https://mlsanity.com/paper/2211.01840/) (2022)
- [Phases, Modalities, Temporal and Spatial Locality: Domain Specific ML Prefetcher for Accelerating Graph Analytics](https://mlsanity.com/paper/2212.05250/) (2022)
- [A Comprehensive Review of Machine Learning Advances on Data Change: A Cross-Field Perspective](https://mlsanity.com/paper/2402.12627/) (2024)
- [An incremental hybrid adaptive network-based IDS in Software Defined Networks to detect stealth attacks](https://mlsanity.com/paper/2404.01109/) (2024)
- [Interpretable Model Drift Detection](https://mlsanity.com/paper/2503.06606/) (2025)
- [Solve Smart, Not Often: Policy Learning for Costly MILP Re-solving](https://mlsanity.com/paper/2509.23470/) (2025)
- [Learner-based Concept Drift Detection: Analysis and Evaluation](https://mlsanity.com/paper/2606.20216/) (2026)
- [Implicit Concept Drift Detection for Multi-label Data Streams](https://mlsanity.com/paper/2202.00070/) (2022)
- [Precise Change Point Detection using Spectral Drift Detection](https://mlsanity.com/paper/2205.06507/) (2022)
- [An Empirical Study of Uncertainty Estimation Techniques for Detecting Drift in Data Streams](https://mlsanity.com/paper/2311.13374/) (2023)
- [Streaming detection of significant delay changes in public transport systems](https://mlsanity.com/paper/2404.07860/) (2024)
- [Drift Detection: Introducing Gaussian Split Detector](https://mlsanity.com/paper/2405.08637/) (2024)
- [Detecting Interpretable Subgroup Drifts](https://mlsanity.com/paper/2408.14682/) (2024)

## Abstract (arXiv)

The amount of real-time communication between agents in an information system has increased rapidly since the beginning of the decade. This is because the use of these systems, e. g. social media, has become commonplace in today's society. This requires analytical algorithms to learn and predict this stream of information in real-time. The nature of these systems is non-static and can be explained, among other things, by the fast pace of trends. This creates an environment in which algorithms must recognize changes and adapt. Recent work shows vital research in the field, but mainly lack stable performance during model adaptation. In this work, a concept drift detection strategy followed by a prototype-based adaptation strategy is proposed. Validated through experimental results on a variety of typical non-static data, our solution provides stable and quick adjustments in times of change.