Scikit-Weak: A Python Library for Weakly Supervised Machine Learning

Campagner, Andrea ; Lienen, Julian ; Hüllermeier, Eyke und Ciucci, Davide (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)
• 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