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...
Gespeichert in:
| Veröffentlicht in: | arXiv.org 2014-09 |
|---|---|
| Hauptverfasser: | , , |
| Format: | Artikel |
| Sprache: | eng |
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| container_end_page | |
|---|---|
| container_issue | |
| container_start_page | |
| container_title | arXiv.org |
| container_volume | |
| creator | McCulloch, Josie Wagner, Christian Aickelin, Uwe |
| 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. |
| format | Article |
| fullrecord | <record><control><sourceid>proquest</sourceid><recordid>TN_cdi_proquest_journals_2084601390</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2084601390</sourcerecordid><originalsourceid>FETCH-proquest_journals_20846013903</originalsourceid><addsrcrecordid>eNqNjUELgjAYQEcQJOV_-KCzMDc1O4YlEXTSu6yaOtGt9rmD_voK-gGd3uE9eAviMc7DII0YWxEfsaOUsmTH4ph75HLQop9Q6QZyN88TFHJEKFtrXNNCZoab0l95lQKdlQimhkINqhdWjRMI_YCjwlHou9yQZS16lP6Pa7LNT2V2Dp7WvJzEseqMs58dVoymUUJDvqf8v-oNzEI8tA</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2084601390</pqid></control><display><type>article</type><title>Analysing Fuzzy Sets Through Combining Measures of Similarity and Distance</title><source>Free E- Journals</source><creator>McCulloch, Josie ; Wagner, Christian ; Aickelin, Uwe</creator><creatorcontrib>McCulloch, Josie ; Wagner, Christian ; Aickelin, Uwe</creatorcontrib><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.</description><identifier>EISSN: 2331-8422</identifier><language>eng</language><publisher>Ithaca: Cornell University Library, arXiv.org</publisher><subject>Fuzzy logic ; Fuzzy sets ; Fuzzy systems ; Reasoning ; Similarity</subject><ispartof>arXiv.org, 2014-09</ispartof><rights>2014. This work is published under http://arxiv.org/licenses/nonexclusive-distrib/1.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>780,784</link.rule.ids></links><search><creatorcontrib>McCulloch, Josie</creatorcontrib><creatorcontrib>Wagner, Christian</creatorcontrib><creatorcontrib>Aickelin, Uwe</creatorcontrib><title>Analysing Fuzzy Sets Through Combining Measures of Similarity and Distance</title><title>arXiv.org</title><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.</description><subject>Fuzzy logic</subject><subject>Fuzzy sets</subject><subject>Fuzzy systems</subject><subject>Reasoning</subject><subject>Similarity</subject><issn>2331-8422</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2014</creationdate><recordtype>article</recordtype><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><recordid>eNqNjUELgjAYQEcQJOV_-KCzMDc1O4YlEXTSu6yaOtGt9rmD_voK-gGd3uE9eAviMc7DII0YWxEfsaOUsmTH4ph75HLQop9Q6QZyN88TFHJEKFtrXNNCZoab0l95lQKdlQimhkINqhdWjRMI_YCjwlHou9yQZS16lP6Pa7LNT2V2Dp7WvJzEseqMs58dVoymUUJDvqf8v-oNzEI8tA</recordid><startdate>20140903</startdate><enddate>20140903</enddate><creator>McCulloch, Josie</creator><creator>Wagner, Christian</creator><creator>Aickelin, Uwe</creator><general>Cornell University Library, arXiv.org</general><scope>8FE</scope><scope>8FG</scope><scope>ABJCF</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>HCIFZ</scope><scope>L6V</scope><scope>M7S</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>PTHSS</scope></search><sort><creationdate>20140903</creationdate><title>Analysing Fuzzy Sets Through Combining Measures of Similarity and Distance</title><author>McCulloch, Josie ; Wagner, Christian ; Aickelin, Uwe</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-proquest_journals_20846013903</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2014</creationdate><topic>Fuzzy logic</topic><topic>Fuzzy sets</topic><topic>Fuzzy systems</topic><topic>Reasoning</topic><topic>Similarity</topic><toplevel>online_resources</toplevel><creatorcontrib>McCulloch, Josie</creatorcontrib><creatorcontrib>Wagner, Christian</creatorcontrib><creatorcontrib>Aickelin, Uwe</creatorcontrib><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>Materials Science & Engineering Collection</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>Technology Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Engineering Collection</collection><collection>Engineering Database</collection><collection>Publicly Available Content Database</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><collection>Engineering Collection</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>McCulloch, Josie</au><au>Wagner, Christian</au><au>Aickelin, Uwe</au><format>book</format><genre>document</genre><ristype>GEN</ristype><atitle>Analysing Fuzzy Sets Through Combining Measures of Similarity and Distance</atitle><jtitle>arXiv.org</jtitle><date>2014-09-03</date><risdate>2014</risdate><eissn>2331-8422</eissn><abstract>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.</abstract><cop>Ithaca</cop><pub>Cornell University Library, arXiv.org</pub><oa>free_for_read</oa></addata></record> |
| fulltext | fulltext |
| identifier | EISSN: 2331-8422 |
| ispartof | arXiv.org, 2014-09 |
| issn | 2331-8422 |
| language | eng |
| recordid | cdi_proquest_journals_2084601390 |
| source | Free E- Journals |
| subjects | Fuzzy logic Fuzzy sets Fuzzy systems Reasoning Similarity |
| title | Analysing Fuzzy Sets Through Combining Measures of Similarity and Distance |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-10T06%3A59%3A24IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest&rft_val_fmt=info:ofi/fmt:kev:mtx:book&rft.genre=document&rft.atitle=Analysing%20Fuzzy%20Sets%20Through%20Combining%20Measures%20of%20Similarity%20and%20Distance&rft.jtitle=arXiv.org&rft.au=McCulloch,%20Josie&rft.date=2014-09-03&rft.eissn=2331-8422&rft_id=info:doi/&rft_dat=%3Cproquest%3E2084601390%3C/proquest%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=2084601390&rft_id=info:pmid/&rfr_iscdi=true |