Inclusion and similarity measures for interval-valued fuzzy sets based on aggregation and uncertainty assessment

We consider the problem of measuring the degree of inclusion and similarity between interval-valued fuzzy sets. We propose a new idea for constructing indicators of inclusion and similarity measures based on the precedence relation, aggregation and uncertainty assessment. Furthermore, we examine selected properties of the suggested measures and their interactions. Finally, we discuss several similarity measures that appear in the literature and compare them with our novel approach. (C) 2020 Elsevier Inc. All rights reserved.

Automated summary

The study focuses on measuring the degree of inclusion and similarity between interval-valued fuzzy sets and introduces a new approach for constructing indicators based on precedence relation, aggregation, and uncertainty assessment. Selected properties and interactions of the proposed measures are examined, followed by a discussion and comparison with existing similarity measures in the literature.