Scikit-Multiflow: A Multi-Output Streaming Framework
Abstract
scikit-multiflow is a framework for learning from data streams and multi-output learning in Python. Conceived to serve as a platform to encourage the democratization of stream learning research, it provides multiple state-of-the-art learning methods, data generators and evaluators for different stream learning problems, including single-output, multi-output and multi-label. scikit-multiflow builds upon popular open source frameworks including scikit-learn, MOA and MEKA. Development follows the FOSS principles. Quality is enforced by complying with PEP8 guidelines, using continuous integration and functional testing.
Cite
Text
Montiel et al. "Scikit-Multiflow: A Multi-Output Streaming Framework." Machine Learning Open Source Software, 2018.Markdown
[Montiel et al. "Scikit-Multiflow: A Multi-Output Streaming Framework." Machine Learning Open Source Software, 2018.](https://mlanthology.org/mloss/2018/montiel2018jmlr-scikitmultiflow/)BibTeX
@article{montiel2018jmlr-scikitmultiflow,
title = {{Scikit-Multiflow: A Multi-Output Streaming Framework}},
author = {Montiel, Jacob and Read, Jesse and Bifet, Albert and Abdessalem, Talel},
journal = {Machine Learning Open Source Software},
year = {2018},
pages = {1-5},
volume = {19},
url = {https://mlanthology.org/mloss/2018/montiel2018jmlr-scikitmultiflow/}
}