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

MCML Authors

Eyke Hüllermeier

Prof. Dr.

Principal Investigator

Artificial Intelligence and Machine Learning

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.

inproceedings

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IJCRS 2022

International Joint Conference on Rough Sets. Suzhou, China, Nov 11-14, 2022.

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Authors

A. Campagner • J. Lienen • E. Hüllermeier • D. Ciucci

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Links

DOI

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Research Area

 A3 | Computational Models