scikit-multilearn

Multi-label classification package for python

Native Python implementation

A native Python implementation of a variety of multi-label classification algorithms. The list includes:

Interface to Meka

For reference purposes and integration needs a Meka wrapper class is implemented. Thus providing access to all methods available in meka, mulan and weka - the reference standard of the field. [0.0.1]

Builds upon giants!

Team up with the power of numpy and scikit. You can use scikit-learn base classifiers as scikit-multilearn's classifiers. Scikit-multilearn classifiers follow the API of scikit-learn classifiers.

Free as in BSD

The licencing model follows scikit's BSD licence, to allow maximum interopability. Repository is set up on github at scikit-multilearn/scikit-multilearn

Join US!

This project has been started by niedakh for the purpose of experiments for his PhD with lots of to Data Science Group at the Wrocław University of Technology. The Python world needs a multi-label classification library, help build one & join our team!

Cite US!

If you use scikit-multilearn in your research and publish it, please consider citing us, it will help us get funding for making the library better.

Szymański P. (2014). Scikit-multilearn: Enhancing multi-label classification in Python. Manuscript in preparation.

Or try the following Bibtex:

@unpublished{scikit-multilearn
title={Scikit-multilearn: Enhancing multi-label classification in Python},
author={Szymański P.},
note = {Manuscript in preparation},
year = {2014}
}