# Scikit-Multiflow: A Multi-output Streaming Framework

arXiv: https://arxiv.org/abs/1807.04662 · HTML: https://mlsanity.com/paper/1807.04662/

## Key facts

- Cited by 70 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Jul 2018.

## Built on by (most cited first, 30 of 70)

- [A survey on learning from imbalanced data streams: taxonomy, challenges, empirical study, and reproducible experimental framework](https://mlsanity.com/paper/2204.03719/) (2022)
- [A Lightweight Concept Drift Detection and Adaptation Framework for IoT Data Streams](https://mlsanity.com/paper/2104.10529/) (2021)
- [Drift-Resilient TabPFN: In-Context Learning Temporal Distribution Shifts on Tabular Data](https://mlsanity.com/paper/2411.10634/) (2024)
- [Detecting Concept Drift With Neural Network Model Uncertainty](https://mlsanity.com/paper/2107.01873/) (2021)
- [Model Based Explanations of Concept Drift](https://mlsanity.com/paper/2303.09331/) (2023)
- [STUDD: A Student-Teacher Method for Unsupervised Concept Drift Detection](https://mlsanity.com/paper/2103.00903/) (2021)
- [PWPAE: An Ensemble Framework for Concept Drift Adaptation in IoT Data Streams](https://mlsanity.com/paper/2109.05013/) (2021)
- [Online Boosting Adaptive Learning under Concept Drift for Multistream Classification](https://mlsanity.com/paper/2312.10841/) (2023)
- [IoT Data Analytics in Dynamic Environments: From An Automated Machine Learning Perspective](https://mlsanity.com/paper/2209.08018/) (2022)
- [A Survey on Failure Analysis and Fault Injection in AI Systems](https://mlsanity.com/paper/2407.00125/) (2024)
- [A Survey on Spatio-temporal Data Analytics Systems](https://mlsanity.com/paper/2103.09883/) (2021)
- [Active Weighted Aging Ensemble for Drifted Data Stream Classification](https://mlsanity.com/paper/2112.10150/) (2021)
- [Suitability of Different Metric Choices for Concept Drift Detection](https://mlsanity.com/paper/2202.09486/) (2022)
- [Change Detection for Local Explainability in Evolving Data Streams](https://mlsanity.com/paper/2209.02764/) (2022)
- [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)
- [Dynamic Model Tree for Interpretable Data Stream Learning](https://mlsanity.com/paper/2203.16181/) (2022)
- [Fast & Furious: Modelling Malware Detection as Evolving Data Streams](https://mlsanity.com/paper/2205.12311/) (2022)
- [MRCpy: A Library for Minimax Risk Classifiers](https://mlsanity.com/paper/2108.01952/) (2021)
- [ACDC: Online Unsupervised Cross-Domain Adaptation](https://mlsanity.com/paper/2110.01326/) (2021)
- [Autoregressive based Drift Detection Method](https://mlsanity.com/paper/2203.04769/) (2022)
- [Standardized Evaluation of Machine Learning Methods for Evolving Data Streams](https://mlsanity.com/paper/2204.13625/) (2022)
- [Adaptive Bernstein Change Detector for High-Dimensional Data Streams](https://mlsanity.com/paper/2306.12974/) (2023)
- [Automatic Learning to Detect Concept Drift](https://mlsanity.com/paper/2105.01419/) (2021)
- [On-the-Fly Ensemble Pruning in Evolving Data Streams](https://mlsanity.com/paper/2109.07611/) (2021)
- [A Broad Ensemble Learning System for Drifting Stream Classification](https://mlsanity.com/paper/2110.03540/) (2021)
- [Unfolding AIS transmission behavior for vessel movement modeling on noisy data leveraging machine learning](https://mlsanity.com/paper/2202.13867/) (2022)
- [DynED: Dynamic Ensemble Diversification in Data Stream Classification](https://mlsanity.com/paper/2308.10807/) (2023)
- [Identifying Predictions That Influence the Future: Detecting Performative Concept Drift in Data Streams](https://mlsanity.com/paper/2412.10545/) (2024)

## Abstract (arXiv)

Scikit-multiflow is a multi-output/multi-label and stream data mining framework for the Python programming language. Conceived to serve as a platform to encourage democratization of stream learning research, it provides multiple state of the art methods for stream learning, stream generators and evaluators. scikit-multiflow builds upon popular open source frameworks including scikit-learn, MOA and MEKA. Development follows the FOSS principles and quality is enforced by complying with PEP8 guidelines and using continuous integration and automatic testing. The source code is publicly available at https://github.com/scikit-multiflow/scikit-multiflow.