Feature selection by a distance measure method of subnormal and non-convex fuzzy sets
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Qu, Letao | - |
dc.contributor.author | Wang, Bohyun | - |
dc.contributor.author | Lim, Joon S. | - |
dc.date.accessioned | 2021-11-21T01:40:30Z | - |
dc.date.available | 2021-11-21T01:40:30Z | - |
dc.date.created | 2021-11-19 | - |
dc.date.issued | 2021-11 | - |
dc.identifier.issn | 1064-1246 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/gachon/handle/2020.sw.gachon/82718 | - |
dc.description.abstract | Distance measures of fuzzy sets have been developed for feature selection and finding redundant features in the fields of decision-making, prediction, and classification problems. Terms commonly used in the definition of fuzzy sets are normal and convex fuzzy sets. This paper extends the general fuzzy set definitions to subnormal and non-convex fuzzy sets that are more precise when implementing uncertain knowledge representations by weighing fuzzy membership functions. A distance measure method for subnormal and non-convex fuzzy sets is proposed for embedded feature selection. Constructing fuzzy membership functions and extracting fuzzy rules play a critical role in fuzzy classification systems. The weighted fuzzy membership functions prevent the combinatorial explosion of fuzzy rules in multiple fuzzy rule-based systems. The proposed method was validated by a comparison with two other methods. Our proposed method demonstrated higher accuracies in training and test, with scores of 97.95% and 93.98%, respectively, compared to the other two methods. © 2021 - IOS Press. | - |
dc.language | 영어 | - |
dc.language.iso | en | - |
dc.publisher | IOS PRESS | - |
dc.relation.isPartOf | JOURNAL OF INTELLIGENT & FUZZY SYSTEMS | - |
dc.title | Feature selection by a distance measure method of subnormal and non-convex fuzzy sets | - |
dc.type | Article | - |
dc.type.rims | ART | - |
dc.description.journalClass | 1 | - |
dc.identifier.wosid | 000716498300049 | - |
dc.identifier.doi | 10.3233/JIFS-219005 | - |
dc.identifier.bibliographicCitation | JOURNAL OF INTELLIGENT & FUZZY SYSTEMS, v.41, no.4, pp.5199 - 5205 | - |
dc.description.isOpenAccess | N | - |
dc.identifier.scopusid | 2-s2.0-85118952131 | - |
dc.citation.endPage | 5205 | - |
dc.citation.startPage | 5199 | - |
dc.citation.title | JOURNAL OF INTELLIGENT & FUZZY SYSTEMS | - |
dc.citation.volume | 41 | - |
dc.citation.number | 4 | - |
dc.contributor.affiliatedAuthor | Qu, Letao | - |
dc.contributor.affiliatedAuthor | Wang, Bohyun | - |
dc.contributor.affiliatedAuthor | Lim, Joon S. | - |
dc.type.docType | Article | - |
dc.subject.keywordAuthor | bounded sum | - |
dc.subject.keywordAuthor | distance measures | - |
dc.subject.keywordAuthor | Embedded feature selection | - |
dc.subject.keywordAuthor | fuzzy neural networks | - |
dc.subject.keywordAuthor | non-covex fuzzy sets | - |
dc.subject.keywordAuthor | sub-normal fuzzy sets | - |
dc.relation.journalResearchArea | Computer Science | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Artificial Intelligence | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
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