Similarity measure for aggregated fuzzy numbers from interval-valued data

TL;DR

This paper introduces a new similarity measure for aggregated fuzzy numbers derived from interval data, utilizing the Interval Agreement Approach and PCA for feature weighting, with an illustrative example.

Contribution

It presents a novel similarity measure for aggregated fuzzy numbers that redefines key attributes and employs PCA for feature weighting.

Findings

New similarity measure for aggregated fuzzy numbers

Features include area, perimeter, centroids, quartiles, agreement ratio

Illustrative example demonstrating application

Abstract

This paper presents a method to compute the degree of similarity between two aggregated fuzzy numbers from intervals using the Interval Agreement Approach (IAA). The similarity measure proposed within this study contains several features and attributes, of which are novel to aggregated fuzzy numbers. The attributes completely redefined or modified within this study include area, perimeter, centroids, quartiles and the agreement ratio. The recommended weighting for each feature has been learned using Principal Component Analysis (PCA). Furthermore, an illustrative example is provided to detail the application and potential future use of the similarity measure.