A method for the detection of the most suitable fuzzy implication for data applications

Fuzzy implications are widely used in applications where propositional logic is applicable. In cases where a variety of fuzzy implications can be used for a specific application, it is important that the optimal candidate to be chosen in order valuable inference to be drawn for a given set of data....

Full description

Saved in:
Bibliographic Details
Published in:Evolving systems 2020-09, Vol.11 (3), p.467-477
Main Authors: Pagouropoulos, Panagiotis, Tzimopoulos, Christos D., Papadopoulos, Basil K.
Format: Article
Language:English
Subjects:
Artificial Intelligence
Complex Systems
Complexity
Engineering
Original Paper
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Fuzzy implications are widely used in applications where propositional logic is applicable. In cases where a variety of fuzzy implications can be used for a specific application, it is important that the optimal candidate to be chosen in order valuable inference to be drawn for a given set of data. This study introduces a method for detecting the most suitable fuzzy implication among others under consideration by evaluating the metric distance between each implication and the ideal implication for a given data application. The ideal implication I is defined and used as a reference in order to measure the suitability of fuzzy implications. The method incorporates an algorithm which results in two extreme cases of fuzzy implications regarding their suitability for inference making; the most suitable and the least suitable implications. An example involving five fuzzy implications is included to illustrate the procedure of the method. The results obtained verify that the resulting implication is the optimal operator for inference making for the data.
ISSN:1868-6478
1868-6486
DOI:10.1007/s12530-018-9233-0