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Campagner, Andrea ORCID logoORCID: https://orcid.org/0000-0002-0027-5157; Lienen, Julian ORCID logoORCID: https://orcid.org/0000-0003-2162-8107; Hüllermeier, Eyke ORCID logoORCID: https://orcid.org/0000-0002-9944-4108 and Ciucci, Davide ORCID logoORCID: https://orcid.org/0000-0002-8083-7809 (2022): Scikit-Weak: A Python Library for Weakly Supervised Machine Learning. International Joint Conference on Rough Sets, Suzhou, China, 11-14 November 2022. In: Rough Sets, Vol. 13633 Cham: Springer. pp. 57-70

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In this article we introduce and describe SCIKIT-WEAK, a Python library inspired by SCIKIT-LEARN and developed to provide an easy-to-use framework for dealing with weakly supervised and imprecise data learning problems, which, despite their importance in real-world settings, cannot be easily managed by existing libraries. We provide a rationale for the development of such a library, then we discuss its design and the currently implemented methods and classes, which encompass several state-of-the-art algorithms.

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