Dealing with imbalanced and weakly labelled data in machine learning using fuzzy and rough set methods

Vluymans, Sarah

Ghent University. Faculty of Medicine and Health Sciences ; University of Granada. Department of Computer Science and Artificial Intelligence

Dealing with imbalanced and weakly labelled data in machine learning using fuzzy and rough set methods

dc:creator Vluymans, Sarah

dc:date2018

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thesis:*

Grantor dc:publisher: Ghent University. Faculty of Medicine and Health Sciences ; University of Granada. Department of Computer Science and Artificial Intelligence
Year dc:date: 2018

Author and committee

dc:creator, dc:contributor.*

Author dc:creator: Vluymans, Sarah
Contributors dc:contributor: Saeys, Yvan
Cornelis, Chris

Subjects

dc:subject × 1

Science General

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Statement dc:rights: info:eu-repo/semantics/openAccess
Language dc:language: eng

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dc:identifier.*

Identifier: https://biblio.ugent.be/publication/8567516
https://biblio.ugent.be/publication/8567516/file/8567517
OAI record oai:identifier: https://biblio.ugent.be/oai?verb=GetRecord&metadataPrefix=oai_dc&identifier=oai%3Aarchive.ugent.be%3A8567516

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Harvested from: Ghent University
Base URL: biblio.ugent.be/oai
Last updated: 2026-07-24
Source record: OAI-PMH GetRecord

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citation

Vluymans, Sarah. Dealing with imbalanced and weakly labelled data in machine learning using fuzzy and rough set methods. Ghent University. Faculty of Medicine and Health Sciences ; University of Granada. Department of Computer Science and Artificial Intelligence, 2018. http://hdl.handle.net/1854/LU-8567516

Dealing with imbalanced and weakly labelled data in machine learning using fuzzy and rough set methods

Degree

Author and committee

Subjects

Rights

Identifiers

Chain of custody

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