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.
Campagner, A., Lienen, J., Hullermeier, E., Ciucci, D. (2022). Scikit-Weak: A Python Library for Weakly Supervised Machine Learning. In Rough Sets : International Joint Conference, IJCRS 2022, Suzhou, China, November 11–14, 2022, Proceedings (pp.57-70). Springer Science and Business Media Deutschland GmbH [10.1007/978-3-031-21244-4_5].
Scikit-Weak: A Python Library for Weakly Supervised Machine Learning
Campagner, A
;Ciucci, D
2022
Abstract
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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