Analysing fuzzy sets through combining measures of similarity and distance

McCulloch, Josie, Wagner, Christian and Aickelin, Uwe (2014) Analysing fuzzy sets through combining measures of similarity and distance. In: 2014 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), 6-11 July 2014, Beijing, China.

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Reasoning with fuzzy sets can be achieved through measures such as similarity and distance. However, these measures can often give misleading results when considered independently, for example giving the same value for two different pairs of fuzzy sets. This is particularly a problem where many fuzzy sets are generated from real data, and while two different measures may be used to automatically compare such fuzzy sets, it is difficult to interpret two different results. This is especially true where a large number of fuzzy sets are being compared as part of a reasoning system. This paper introduces a method for combining the results of multiple measures into a single measure for the purpose of analysing and comparing fuzzy sets. The combined measure alleviates ambiguous results and aids in the automatic comparison of fuzzy sets. The properties of the combined measure are given, and demonstrations are presented with discussions on the advantages over using a single measure.

Item Type: Conference or Workshop Item (Paper)
Additional Information: Published in: 2014 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). Piscataway, NJ : IEEE,[2014. (ISBN: 9781479920730), pp. 155-162 (doi: 10.1109/FUZZ-IEEE.2014.6891672). © 2014 IEEE
Keywords: Fuzzy, Logic
Schools/Departments: University of Nottingham, UK > Faculty of Science > School of Computer Science
Depositing User: Aickelin, Professor Uwe
Date Deposited: 30 Sep 2014 11:35
Last Modified: 04 May 2020 16:54

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