A scikit-based Python environment for performing multi-label
classification
release_buzoouag3bd5xowjivy32a5une
by
Piotr Szymański, Tomasz Kajdanowicz
2018
Abstract
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 a novel
framework for label space partitioning and division. It includes modern
algorithm adaptation methods, network-based label space division approaches,
which extracts label dependency information and multi-label embedding
classifiers. It provides python wrapped access to the extensive multi-label
method stack from Java libraries and makes it possible to extend deep learning
single-label methods for multi-label tasks. The library allows multi-label
stratification and data set management. The implementation is more efficient in
problem transformation than other established libraries, has good test coverage
and follows PEP8. Source code and documentation can be downloaded from
http://scikit.ml and also via pip. The library follows BSD licensing scheme.
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