Scikit-Multiflow: A Multi-output Streaming Framework

By Jacob Montiel, Jesse Read, Albert Bifet, Talel Abdessalem. Journal of machine learning research, 2018

349 citations recorded by the source corpus.

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 this https URL.

View Paper: https://www.semanticscholar.org/paper/e63f34f82bab152422fd2df21a3b787166277a98

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. Heitor Murilo Gomes
2. Jacob Montiel
3. Talel Abdessalem
4. Max Halford
5. S. Mastelini
6. Geoffrey Bolmier
7. Raphael Sourty
8. Robin Vaysse

Citations in the corpus

• River: machine learning for streaming data in Python (2020, background): “Creme (Halford et al., 2019) and scikit-multiflow (Montiel et al., 2018) are two opensource libraries to perform machine learning in the stream setting.”