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Feature Selection and Disambiguation in Learning from Fuzzy Labels Using Rough Sets

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Book cover Rough Sets (IJCRS 2021)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 12872))

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Abstract

In this article, we study the setting of learning from fuzzy labels, a generalization of supervised learning in which instances are assumed to be labeled with a fuzzy set, interpreted as an epistemic possibility distribution. We tackle the problem of feature selection in such task, in the context of rough set theory (RST). More specifically, we consider the problem of RST-based feature selection as a means for data disambiguation: that is, retrieving the most plausible precise instantiation of the imprecise training data. We define generalizations of decision tables and reducts, using tools from generalized information theory and belief function theory. We study the computational complexity and theoretical properties of the associated computational problems.

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Notes

  1. 1.

    We note that in the learning from fuzzy labels setting, the set of candidate labels (that is, the labels with a membership degree greater than 0) is given a disjunctive interpretation: only one of those labels is correct, but we don’t precisely know which one, and the membership degrees represent degrees of belief. Thus, in this article, we do not consider the conjunctive interpretation, in which the membership degrees are degrees of truth (and, thus, could be seen as a generalization of multi-label learning).

  2. 2.

    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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Authors and Affiliations

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

    Andrea Campagner & Davide Ciucci

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  1. Andrea Campagner
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  2. Davide Ciucci
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Correspondence to Andrea Campagner .

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Editors and Affiliations

  1. University of Winnipeg, Winnipeg, MB, Canada

    Sheela Ramanna

  2. Ghent University, Gent, Belgium

    Chris Cornelis

  3. University of Milano-Bicocca, Milan, Italy

    Davide Ciucci

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Campagner, A., Ciucci, D. (2021). Feature Selection and Disambiguation in Learning from Fuzzy Labels Using Rough Sets. In: Ramanna, S., Cornelis, C., Ciucci, D. (eds) Rough Sets. IJCRS 2021. Lecture Notes in Computer Science(), vol 12872. Springer, Cham. https://doi.org/10.1007/978-3-030-87334-9_14

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  • DOI: https://doi.org/10.1007/978-3-030-87334-9_14

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