scikit-multilearn - A Python library for Multi-Label Classification

Abstract

The scikit-multilearn is a Python library for performing multi-label classification. It is compatible with the scikit-learn and scipy ecosystems and uses sparse matrices for all internal operations; provides native Python implementations of popular multi-label classification methods alongside a novel framework for label space partitioning and division and includes modern algorithm adaptation methods, network-based label space division approaches, which extracts label dependency information and multi-label embedding classifiers. The library 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. The project is BSD-licensed.

Publication
Journal of Machine Learning Research, 20(6) 1-22
Piotr Szymański
Piotr Szymański
Associate Professor

Piotr Szymański is an assistant Professor at the Department of Computational Intelligence at the Wrocław University of Science and Technology and a Machine Learning Engineer at Avaya. Professionally involved in data analysis, statistical reasoning, geospatial data science, natural language processing, machine learning and artificial intelligence techniques.

Tomasz Kajdanowicz
Tomasz Kajdanowicz
Associate Professor, head of faculty’s doctoral studies, head of department

My research interests include representation learning, social network and media analysis, and machine learning.

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