Description:

scikit-multilearn is a Python library for performing multi-label classification. The library is compatible with the scikit/scipy ecosystem and uses sparse matrices for all internal operations. It provides native Python implementations of popular multi-label classification methods alongside novel framework for label space partitioning and division. It includes graph-based community detection methods that utilize the powerful igraph library for extracting label dependency information. In addition its code is well test covered and follows PEP8. Source code and documentation can be downloaded from http://scikit.ml and also via pip. The library follows scikit's BSD licencing scheme.

Changes to previous version:

• a general matrix-based label space clusterer has been added which can cluster the output space using any scikit-learn compatible clusterer (incl. k-means)
• support for more single-class and multi-class classifiers you can now use problem transformation approaches with your favourite neural networks/deep learning libraries: theano, tensorflow, keras, scikit-neuralnetworks
• support for label powerset based stratified kfold added
• graph-tool clusterer supports weighted graphs again and includes stochastic blockmodel calibration
• bugs were fixed in: classifier chains and hierarchical neuro fuzzy clasifiers

BibTeX Entry: Download

Corresponding Paper BibTeX Entry: Download

Supported Operating Systems: Agnostic

Data Formats: Arff, Numpy, Scipy

Tags: Machine Learning, Large Datasets, Multi Label

Archive: download here

Other available revisons

Version	Changelog	Date
0.0.5	a general matrix-based label space clusterer has been added which can cluster the output space using any scikit-learn compatible clusterer (incl. k-means) support for more single-class and multi-class classifiers you can now use problem transformation approaches with your favourite neural networks/deep learning libraries: theano, tensorflow, keras, scikit-neuralnetworks support for label powerset based stratified kfold added graph-tool clusterer supports weighted graphs again and includes stochastic blockmodel calibration bugs were fixed in: classifier chains and hierarchical neuro fuzzy clasifiers	February 25, 2017, 03:51:59
0.0.4	*kNN classifiers support sparse matrices properly support for the new model_selection API from scikit-learn extended graph-based label space clusteres to allow taking probability of a label occuring alone into consideration compatible with newest graphtool support the case when meka decides that an observation doesn't have any labels assigned HARAM classifier provided by Fernando Benitez from University of Konstanz predict_proba added to problem transformation classifiers ported to python 3	February 15, 2017, 21:11:40
0.0.3	Initial Announcement on mloss.org.	June 15, 2016, 19:28:32

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