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Rough-set Based Genetic Algorithms for Weakly Supervised Feature Selection

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Information Processing and Management of Uncertainty in Knowledge-Based Systems (IPMU 2022)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1602))

Abstract

In this article, we study the problem of feature selection under weak supervision, focusing in particular on the fuzzy labels setting, where the weak supervision is provided in terms of possibility distributions over candidate labels. While traditional Rough Set-based approaches have been applied for tackling this problem, they have high computational complexity and only provide local search heuristic methods. In order to address these issues, we propose a global optimization algorithm, based on genetic algorithms and Rough Set theory, for feature selection under fuzzy labels. Based on a set of experiments, we illustrate the effectiveness of the proposed approach in comparison to state-of-the-art methods.

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Notes

  1. 1.

    Here \(sup_{\le _{C}}\mathcal {I}(R) = \{ I \in \mathcal {I}(R) : \not \exists I' \in \mathcal {I}(R) \text { s.t. } I <_C I' \}\).

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Author information

Authors and Affiliations

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

    Andrea Campagner & Davide Ciucci

Authors
  1. Andrea Campagner
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  2. Davide Ciucci
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Corresponding author

Correspondence to Andrea Campagner .

Editor information

Editors and Affiliations

  1. University of Milano-Bicocca, Milan, Italy

    Davide Ciucci

  2. University of Oviedo, Oviedo, Spain

    Inés Couso

  3. University of Cádiz, Cádiz, Spain

    Jesús Medina

  4. University of Warsaw, Warsaw, Poland

    Dominik Ślęzak

  5. University of Perugia, Perugia, Italy

    Davide Petturiti

  6. Sorbonne Université, Paris, France

    Bernadette Bouchon-Meunier

  7. Iona College, New Rochelle, NY, USA

    Ronald R. Yager

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Campagner, A., Ciucci, D. (2022). Rough-set Based Genetic Algorithms for Weakly Supervised Feature Selection. In: Ciucci, D., et al. Information Processing and Management of Uncertainty in Knowledge-Based Systems. IPMU 2022. Communications in Computer and Information Science, vol 1602. Springer, Cham. https://doi.org/10.1007/978-3-031-08974-9_60

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  • DOI: https://doi.org/10.1007/978-3-031-08974-9_60

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-08973-2

  • Online ISBN: 978-3-031-08974-9

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