Approaches to learning strictly-stable weights for data with missing values

DOI10.1016/j.fss.2017.02.003zbMath1380.68350OpenAlexW2588324678MaRDI QIDQ1697618

Gleb Beliakov, Simon James, Javier Montero, Daniel Gómez, J. Tinguaro Rodríguez

Publication date: 20 February 2018

Published in: Fuzzy Sets and Systems (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.fss.2017.02.003

zbMATH Keywords

linear programmingaggregation functionsmissing datastrict stabilityweight learning

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05) Reasoning under uncertainty in the context of artificial intelligence (68T37)

Related Items (4)

Recent fuzzy generalisations of rough sets theory: a systematic review and methodological critique of the literature ⋮ Computable aggregations ⋮ A generalization of stability for families of aggregation operators ⋮ Weighted aggregation systems and an expectation level-based weighting and scoring procedure

Uses Software

• R
• LBFGS-B

Cites Work

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• Noncommutative self-identity aggregation
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• Stability of weighted penalty-based aggregation functions
• Strictly stable families of aggregation operators
• GENERATION OF WEIGHTING TRIANGLES ASSOCIATED WITH AGGREGATION FUNCTIONS
• A Limited Memory Algorithm for Bound Constrained Optimization
• Fitting Aggregation Functions to Data: Part I - Linearization and Regularization
• On mean values
• Representation of consistent recursive rules.

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