Analysing fuzzy sets through combining measures of similarity and distance
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 fuzz...
| Main Authors: | , , |
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| Format: | Conference or Workshop Item |
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2014
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| Online Access: | https://eprints.nottingham.ac.uk/3353/ |
| _version_ | 1848791021552402432 |
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| author | McCulloch, Josie Wagner, Christian Aickelin, Uwe |
| author_facet | McCulloch, Josie Wagner, Christian Aickelin, Uwe |
| author_sort | McCulloch, Josie |
| building | Nottingham Research Data Repository |
| collection | Online Access |
| description | 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. |
| first_indexed | 2025-11-14T18:21:53Z |
| format | Conference or Workshop Item |
| id | nottingham-3353 |
| institution | University of Nottingham Malaysia Campus |
| institution_category | Local University |
| last_indexed | 2025-11-14T18:21:53Z |
| publishDate | 2014 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | nottingham-33532020-05-04T16:54:27Z https://eprints.nottingham.ac.uk/3353/ Analysing fuzzy sets through combining measures of similarity and distance McCulloch, Josie Wagner, Christian Aickelin, Uwe 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. 2014-09-08 Conference or Workshop Item PeerReviewed 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. Fuzzy Logic http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6891672 |
| spellingShingle | Fuzzy Logic McCulloch, Josie Wagner, Christian Aickelin, Uwe Analysing fuzzy sets through combining measures of similarity and distance |
| title | Analysing fuzzy sets through combining measures of similarity and distance |
| title_full | Analysing fuzzy sets through combining measures of similarity and distance |
| title_fullStr | Analysing fuzzy sets through combining measures of similarity and distance |
| title_full_unstemmed | Analysing fuzzy sets through combining measures of similarity and distance |
| title_short | Analysing fuzzy sets through combining measures of similarity and distance |
| title_sort | analysing fuzzy sets through combining measures of similarity and distance |
| topic | Fuzzy Logic |
| url | https://eprints.nottingham.ac.uk/3353/ https://eprints.nottingham.ac.uk/3353/ |