A scikit-based Python environment for performing multi-label classification

TL;DR

scikit-multilearn is a Python library that facilitates multi-label classification with efficient algorithms, label space partitioning, and integration with deep learning methods, all within the scikit ecosystem.

Contribution

It introduces a comprehensive, efficient, and extendable Python library for multi-label classification, including novel label space partitioning frameworks and deep learning integration.

Findings

More efficient problem transformation than existing libraries

Supports deep learning extensions for multi-label tasks

Provides extensive multi-label classification methods and tools

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.