.png){kind=icon}
.png){kind=icon}
Return
Characterizing Quantifier Fuzzification Mechanisms: A behavioral guide for applications
.png){kind=icon}
OA
AI
DOI：10.1016/j.fss.2017.07.017.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Important advances have been made in the fuzzy quantification field. Nevertheless, some problems remain when we face the decision of selecting the most convenient model for a specific application. In the literature, several desirable adequacy properties have been proposed, but theoretical limits impede quantification models from simultaneously fulfilling every adequacy property that has been defined. Besides, the complexity of model definitions and adequacy properties makes very difficult for real users to understand the particularities of the different models that have been presented. In this work we will present several criteria conceived to help in the process of selecting the most adequate Quantifier Fuzzification Mechanisms for specific practical applications. In addition, some of the best known well-behaved models will be compared against this list of criteria. Based on this analysis, some guidance to choose fuzzy quantification models for practical applications will be provided. (C) 2017 Elsevier B.V. All rights reserved.
Keywords:
Fuzzy quantification
Determiner fuzzification schemes
Theory of generalized quantifiers
Quantifier Fuzzification Mechanism
Applications of fuzzy quantification
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
2.7
Papers:
7.6K
Citations:
1.5W