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.

Campagner, A., Ciucci, D. (2021). Feature Selection and Disambiguation in Learning from Fuzzy Labels Using Rough Sets. In IJCRS: International Joint Conference on Rough Sets (pp.164-179). Springer Science and Business Media Deutschland GmbH [10.1007/978-3-030-87334-9_14].

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

Campagner, A
;
Ciucci, D
2021

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.
No
paper
Belief functions; Entropy; Feature selection; Fuzzy labels; Rough sets;
English
International Joint Conference on Rough Sets, IJCRS 2021 - 19 September 2021 through 24 September 2021
978-3-030-87333-2
2021
Campagner, A., Ciucci, D. (2021). Feature Selection and Disambiguation in Learning from Fuzzy Labels Using Rough Sets. In IJCRS: International Joint Conference on Rough Sets (pp.164-179). Springer Science and Business Media Deutschland GmbH [10.1007/978-3-030-87334-9_14].
Campagner, A; Ciucci, D
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10281/331712
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