Evolutionary Instance Selection Algorithm based on Takagi-Sugeno Fuzzy Model
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Lee, Sang-Hong | - |
dc.contributor.author | Lim, Joon S. | - |
dc.date.available | 2020-02-28T17:43:30Z | - |
dc.date.created | 2020-02-06 | - |
dc.date.issued | 2014-05 | - |
dc.identifier.issn | 2325-0399 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/gachon/handle/2020.sw.gachon/12636 | - |
dc.description.abstract | In this study, we propose evolutionary instance selection based on the Takagi-Sugeno (T-S) fuzzy model. The previous neural network with weighted fuzzy membership functions (NEWFM) supports feature selection; thus, it enables the selection of minimum features with the highest performance. The enhanced NEWFM supports a weighted mean defuzzification in the T-S fuzzy model with a confidence interval in the normal distribution; thus, it enables the selection of minimum instances with the highest performance. The enhanced NEWFM has two stages; feature selection is performed in the first stage, whereas instance selection is performed in the second stage. The performance of the enhanced NEWFM is compared with that of the previous NEWFM. In addition, McNemar's test reveals a significant difference between the performances of both NEWFMs (p < 0.05). | - |
dc.language | 영어 | - |
dc.language.iso | en | - |
dc.publisher | NATURAL SCIENCES PUBLISHING CORP-NSP | - |
dc.relation.isPartOf | APPLIED MATHEMATICS & INFORMATION SCIENCES | - |
dc.subject | NEURAL-NETWORK SYSTEM | - |
dc.subject | CLASSIFIERS | - |
dc.subject | FEATURES | - |
dc.subject | RULE | - |
dc.title | Evolutionary Instance Selection Algorithm based on Takagi-Sugeno Fuzzy Model | - |
dc.type | Article | - |
dc.type.rims | ART | - |
dc.description.journalClass | 1 | - |
dc.identifier.wosid | 000331387600046 | - |
dc.identifier.doi | 10.12785/amis/080346 | - |
dc.identifier.bibliographicCitation | APPLIED MATHEMATICS & INFORMATION SCIENCES, v.8, no.3, pp.1307 - 1312 | - |
dc.identifier.scopusid | 2-s2.0-84893100640 | - |
dc.citation.endPage | 1312 | - |
dc.citation.startPage | 1307 | - |
dc.citation.title | APPLIED MATHEMATICS & INFORMATION SCIENCES | - |
dc.citation.volume | 8 | - |
dc.citation.number | 3 | - |
dc.contributor.affiliatedAuthor | Lim, Joon S. | - |
dc.type.docType | Article | - |
dc.subject.keywordAuthor | Instance selection | - |
dc.subject.keywordAuthor | feature selection | - |
dc.subject.keywordAuthor | Takagi-Sugeno fuzzy model | - |
dc.subject.keywordAuthor | McNemar&apos | - |
dc.subject.keywordAuthor | s test | - |
dc.subject.keywordAuthor | normal distribution | - |
dc.subject.keywordPlus | NEURAL-NETWORK SYSTEM | - |
dc.subject.keywordPlus | CLASSIFIERS | - |
dc.subject.keywordPlus | FEATURES | - |
dc.subject.keywordPlus | RULE | - |
dc.relation.journalResearchArea | Mathematics | - |
dc.relation.journalResearchArea | Physics | - |
dc.relation.journalWebOfScienceCategory | Mathematics, Applied | - |
dc.relation.journalWebOfScienceCategory | Physics, Mathematical | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.
1342, Seongnam-daero, Sujeong-gu, Seongnam-si, Gyeonggi-do, Republic of Korea(13120)031-750-5114
COPYRIGHT 2020 Gachon University All Rights Reserved.
Certain data included herein are derived from the © Web of Science of Clarivate Analytics. All rights reserved.
You may not copy or re-distribute this material in whole or in part without the prior written consent of Clarivate Analytics.