A scikit-based Python environment for performing multi-label classification
This provides a practical tool for researchers and practitioners working on multi-label classification tasks, though it is incremental as it builds on existing methods and libraries.
The authors developed scikit-multilearn, a Python library for multi-label classification that integrates with the scikit/scipy ecosystem and uses sparse matrices for efficiency. It implements various multi-label methods, includes a novel label space partitioning framework, and demonstrates improved efficiency in problem transformation compared to other libraries.
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.