ORCID: https://orcid.org/0000-0002-0027-5157; Lienen, Julian
ORCID: https://orcid.org/0000-0003-2162-8107; Hüllermeier, Eyke
ORCID: https://orcid.org/0000-0002-9944-4108 und Ciucci, Davide
ORCID: 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,
Bd. 13633
Cham: Springer. S. 57-70
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.
Dokumententyp: | Konferenzbeitrag (Paper) |
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Publikationsform: | Publisher's Version |
Fakultät: | Mathematik, Informatik und Statistik > Informatik > Künstliche Intelligenz und Maschinelles Lernen |
Themengebiete: | 000 Informatik, Informationswissenschaft, allgemeine Werke > 000 Informatik, Wissen, Systeme |
ISSN: | 0302-9743 |
Ort: | Cham |
Sprache: | Englisch |
Dokumenten ID: | 94667 |
Datum der Veröffentlichung auf Open Access LMU: | 16. Feb. 2023, 14:48 |
Letzte Änderungen: | 16. Feb. 2023, 14:48 |