Characterizing Quantifier Fuzzification Mechanisms: a behavioral guide for practical applications

TL;DR

This paper provides a behavioral guide for selecting appropriate quantifier fuzzification mechanisms in practical applications, addressing the complexity and adequacy property trade-offs among models.

Contribution

It introduces criteria to evaluate and compare fuzzy quantification models, aiding users in choosing suitable models for specific practical needs.

Findings

Comparison of well-known models against proposed criteria

Guidelines for selecting quantification models in practice

Analysis of adequacy property trade-offs

Abstract

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.