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

Campagner, Andrea ; Lienen, Julian ; Hüllermeier, Eyke and 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, Vol. 13633 Cham: Springer. pp. 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.

• Item Type: Conference or Workshop Item (Paper)
• Form of publication: Publisher's Version
• Faculties: Mathematics, Computer Science and Statistics > Computer Science > Artificial Intelligence and Machine Learning
• Subjects: 000 Computer science, information and general works > 000 Computer science, knowledge, and systems
• ISSN: 0302-9743
• Place of Publication: Cham
• Language: English