Elsevier

Soft Computing Letters

Volume 2, August 2020, 100002
Soft Computing Letters

Similarity measure for aggregated fuzzy numbers from interval-valued data

https://doi.org/10.1016/j.socl.2020.100002Get rights and content
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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.

Keywords

Similarity measure
Fuzzy logic
Interval agreement approach
IAA, Interval-valued data
Uncertainty

Cited by (0)

Justin Kane Gunn holds a Bachelor of Computer Science (BCS) from Monash University, and has recently completed his MSc thesis in the School of Computing and Information Systems at the University of Melbourne. Having worked as an Associate Lecturer at RMIT University, he currently works as a Lecturer and Course Coordinator at Stott's College whilst completing his MSc. His Research Interests include Machine Learning, Modelling, Uncertainty, Decision Making and Artificial Intelligence (Game Theory).

Hadi A. Khorshidi received his Ph.D from Monash University, where he worked as Research Fellow and Senior Data Analyst. He currently works as a Research Fellow in School of Computing and Information Systems at the University of Melbourne. His research areas include Decision Making and Optimization, Data Mining and Machine Learning, Modelling, Uncertainty and Digital Health, where he has published several papers.

Uwe Aickelin holds a Ph.D degree from the University of Wales (UK). He is now a Professor and Head of School of Computing and Information Systems at the University of Melbourne. He is an Associate Editor of IEEE Transactions on Evolutionary Computation. His Research Interests include Artificial Intelligence (Modelling and Simulation), Data Mining and Machine Learning (Robustness and Uncertainty), Decision Support and Optimisation (Medicine and Digital Economy) and Health Informatics (Electronic Healthcare Records).