The three-way-in and three-way-out framework to treat and exploit ambiguity in data

DOI10.1016/j.ijar.2020.01.010zbMath1434.68568OpenAlexW3001821705WikidataQ126306060 ScholiaQ126306060MaRDI QIDQ2300462

Andrea Campagner, Federico Cabitza, Davide Ciucci

Publication date: 27 February 2020

Published in: International Journal of Approximate Reasoning (Search for Journal in Brave)

Full work available at URL: http://hdl.handle.net/10281/259244

zbMATH Keywords

uncertaintyambiguitymachine learningthree-way decisionpartial labels

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05) Medical applications (general) (92C50) Reasoning under uncertainty in the context of artificial intelligence (68T37)

Related Items (12)

Symbols-meaning-value (SMV) space as a basis for a conceptual model of data science ⋮ Orthopartitions and possibility distributions ⋮ A three-way decision based construction of shadowed sets from atanassov intuitionistic fuzzy sets ⋮ A novel approach of three-way decisions with information interaction strategy for intelligent decision making under uncertainty ⋮ Three-way decision and conformal prediction: isomorphisms, differences and theoretical properties of cautious learning approaches ⋮ Orthopartitions in knowledge representation and machine learning ⋮ A novel three-way decision approach in decision information systems ⋮ Comprehensive fuzzy concept-oriented three-way decision and its application ⋮ Three-way recommendation model based on shadowed set with uncertainty invariance ⋮ Ground truthing from multi-rater labeling with three-way decision and possibility theory ⋮ Editorial. Formal concept analysis, rough sets, and three-way decisions ⋮ Belief functions and rough sets: survey and new insights

Uses Software

• LERS
• Rseslib

Cites Work

• Unnamed Item
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• Rough sets: some extensions
• Decision making under uncertainty using imprecise probabilities
• The transferable belief model
• Three-way decision and granular computing
• A sequential three-way approach to multi-class decision
• Multi-objective attribute reduction in three-way decision-theoretic rough set model
• Three-way and semi-supervised decision tree learning based on orthopartitions
• Three-way group decisions with interval-valued decision-theoretic rough sets based on aggregating inclusion measures
• Generalized multi-granulation double-quantitative decision-theoretic rough set of multi-source information system
• Multi-attribute group decision-making method based on multi-granulation weights and three-way decisions
• Cost-sensitive sequential three-way decision modeling using a deep neural network
• A two-step rejection procedure for testing multiple hypotheses
• Learning from imprecise and fuzzy observations: data disambiguation through generalized loss minimization
• An Outline of a Theory of Three-Way Decisions
• Advances in Intelligent Data Analysis VI

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