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International Joint Conference on Rough Sets

IJCRS 2022: Rough Sets pp 57–70Cite as

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

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 13633))

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.

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Notes

  1. 1.

    https://scikit-learn.org.

  2. 2.

    Compared to the usual definition of a training set considered in the ML literature the definition of a decision table in rough set theory distinguishes instances in U from their representation in terms of features.

  3. 3.

    https://github.com/AndreaCampagner/scikit-weak.

  4. 4.

    https://pypi.org/project/scikit-weak/.

  5. 5.

    https://sphinx-doc.org/.

  6. 6.

    https://scikit-weak.readthedocs.io.

  7. 7.

    https://tensorflow.org.

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Author information

Authors and Affiliations

  1. Dipartimento di Informatica, Sistemistica e Comunicazione, University of Milano–Bicocca, Viale Sarca 336/14, 20126, Milano, Italy

    Andrea Campagner & Davide Ciucci

  2. Department of Computer Science, Paderborn University, Warburger Str. 100, 33098, Paderborn, Germany

    Julian Lienen

  3. Institute of Informatics, University of Munich (LMU), Munich Center for Machine Learning (MCML), Akademiestr. 7, 80799, Munich, Germany

    Eyke Hüllermeier

Authors
  1. Andrea Campagner
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  2. Julian Lienen
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  3. Eyke Hüllermeier
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  4. Davide Ciucci
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Corresponding author

Correspondence to Andrea Campagner .

Editor information

Editors and Affiliations

  1. University of Regina, Regina, SK, Canada

    JingTao Yao

  2. Iwate Prefectural University, Takizawa, Iwate, Japan

    Hamido Fujita

  3. Shanghai University, Shanghai, China

    Xiaodong Yue

  4. Tongji University, Shanghai, China

    Duoqian Miao

  5. University of Kansas, Lawrence, KS, USA

    Jerzy Grzymala-Busse

  6. Soochow University, Suzhou, Jiangsu, China

    Fanzhang Li

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Campagner, A., Lienen, J., Hüllermeier, E., Ciucci, D. (2022). Scikit-Weak: A Python Library for Weakly Supervised Machine Learning. In: Yao, J., Fujita, H., Yue, X., Miao, D., Grzymala-Busse, J., Li, F. (eds) Rough Sets. IJCRS 2022. Lecture Notes in Computer Science(), vol 13633. Springer, Cham. https://doi.org/10.1007/978-3-031-21244-4_5

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  • DOI: https://doi.org/10.1007/978-3-031-21244-4_5

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  • Online ISBN: 978-3-031-21244-4

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