Similarity measure for aggregated fuzzy numbers from interval-valued data
Justin Kane Gunn, Hadi Akbarzadeh Khorshidi, Uwe Aickelin
Soft Computing Letters | Elsevier BV | Published : 2020
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.