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Three-Way Decisions with Evaluative Linguistic Expressions

Three-way decisions with evaluative linguistic expressions
descriptionPublicationkeyboard_double_arrow_right Article , Preprint 01 Jan 2023Embargo end date: 01 Jan 2023 Italy Publisher:Elsevier BVJournal:International Journal of Approximate Reasoning, volume 164, page 109,080 (issn: 0888-613X, Copyright policy )
Authors: Boffa, S; Ciucci, D;

doi: 10.2139/ssrn.4503447 , 10.1016/j.ijar.2023.109080 , 10.48550/arxiv.2307.11766

arXiv: 2307.11766

handle: 10281/457938

Three-Way Decisions with Evaluative Linguistic Expressions

Abstract

We propose a linguistic interpretation of three-way decisions, where the regions of acceptance, rejection, and non-commitment are constructed by using the so-called evaluative linguistic expressions, which are expressions of natural language such as small, medium, very short, quite roughly strong, extremely good, etc. Our results highlight new connections between two different research areas: three-way decisions and the theory of evaluative linguistic expressions.

Country
Italy
Related Organizations
Keywords

FOS: Computer and information sciences, Evaluative linguistic expressions; Explainable Artificial Intelligence; Probabilistic rough sets; Rough sets; Three-way decisions;, Computer Science - Computation and Language, probabilistic rough sets, explainable artificial intelligence, rough sets, three-way decisions, Reasoning under uncertainty in the context of artificial intelligence, Computation and Language (cs.CL), evaluative linguistic expressions

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    Impact byBIP!
    selected citations
    These citations are derived from selected sources.
    This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    		 7
    popularity
    This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
    		 Top 10%
    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    		 Average
    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
    		 Top 10%
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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
7
Top 10%
Average
Top 10%
Green