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.
paper
Generalized risk minimization; Imprecise data; Imprecisiation; Rough sets; Weakly supervised learning;
English
International Joint Conference on Rough Sets, IJCRS 2022 - 11 November 2022 through 14 November 2022
2022
Yao, J; Fujita, H; Yue, X; Miao, D; Grzymala-Busse, J; Li, F
Rough Sets : International Joint Conference, IJCRS 2022, Suzhou, China, November 11–14, 2022, Proceedings
978-3-031-21243-7
2022
13633 LNAI
57
70
reserved
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].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10281/402355
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