Similarity measure for aggregated fuzzy numbers from interval-valued data
This work addresses the problem of comparing aggregated fuzzy numbers for researchers and practitioners working with interval-valued data, offering an incremental improvement to existing similarity measures.
This paper proposes a new method to measure the similarity between two aggregated fuzzy numbers derived from interval-valued data. It redefines or modifies several attributes such as area, perimeter, centroids, quartiles, and agreement ratio for this purpose.
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.