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.

Campagner, A., Ciucci, D. (2022). Rough-set Based Genetic Algorithms for Weakly Supervised Feature Selection. In Communications in Computer and Information Science (pp.761-773). Springer Science and Business Media Deutschland GmbH [10.1007/978-3-031-08974-9_60].

Rough-set Based Genetic Algorithms for Weakly Supervised Feature Selection

Campagner A.;Ciucci D.
2022

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.
paper
Feature selection; Fuzzy labels; Genetic algorithms; Rough sets; Weak supervision
English
IPMU 2022, 19th International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems, Milano, July 11-15, 2022
2022
Communications in Computer and Information Science
978-3-031-08973-2
2022
1602
761
773
none
Campagner, A., Ciucci, D. (2022). Rough-set Based Genetic Algorithms for Weakly Supervised Feature Selection. In Communications in Computer and Information Science (pp.761-773). Springer Science and Business Media Deutschland GmbH [10.1007/978-3-031-08974-9_60].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10281/394399
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