Granular approximations: a novel statistical learning approach for handling data inconsistency with respect to a fuzzy relation

DOI10.1016/j.ins.2023.01.119OpenAlexW4318773660MaRDI QIDQ6127117

Greco, Salvatore, Chris Cornelis, Marko Palangetić, Slowinski, Roman

Publication date: 10 April 2024

Published in: Information Sciences (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.ins.2023.01.119

zbMATH Keywords

fuzzy logicrough setsstatistical learninginconsistencies in data

Mathematics Subject Classification ID

Statistics (62-XX) Computer science (68-XX)

Cites Work

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• Set-based representations of conjunctive and disjunctive knowledge
• Fuzzy-rough nearest neighbour classification and prediction
• Understanding and using linear programming
• Stochastic orders
• Stochastic dominance-based rough set model for ordinal classification
• Fuzzy sets as a basis for a theory of possibility
• Toward a theory of fuzzy information granulation and its centrality in human reasoning and fuzzy logic
• Rough sets meet statistics -- a new view on rough set reasoning about numerical data
• Rough set-based feature selection for weakly labeled data
• Fuzzy extensions of the dominance-based rough set approach
• Attribute reduction with fuzzy rough self-information measures
• Isotonic Separation
• ROUGH FUZZY SETS AND FUZZY ROUGH SETS*
• Rough sets
• The Isotonic Regression Problem and Its Dual
• Fuzzy random variables
• Rough sets theory for multicriteria decision analysis

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