Density based Fuzzy Support Vector Machines for Multicategory Pattern Classification
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Rhee, Frank Chung-Hoon | - |
dc.contributor.author | Park, Jong Hoon | - |
dc.contributor.author | Choi, Byung In | - |
dc.date.accessioned | 2021-06-23T19:39:38Z | - |
dc.date.available | 2021-06-23T19:39:38Z | - |
dc.date.issued | 2007-06 | - |
dc.identifier.issn | 1615-3871 | - |
dc.identifier.issn | 1860-0794 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/erica/handle/2021.sw.erica/43663 | - |
dc.description.abstract | Support vector machines (SVMs) are known to be useful for separating data into two classes. However, for the multiclass case where pairwise SVMs are incorporated, unclassifiable regions can exist. To solve this problem, Fuzzy support vector machines (FSVMs) was proposed, where membership values are assigned according to the distance between patterns and the hyperplanes obtained by the “crisp” SVM. However, they still may not give proper decision boundaries for arbitrary distributed data sets. In this paper, a density based fuzzy support vector machine (DFSVM) is proposed, which incorporates the data distribution in addition to using the memberships in FSVM. As a result, our proposed algorithm may give more appropriate decision boundaries than FSVM. To validate our proposed algorithm, we show experimental results for several data sets. | - |
dc.format.extent | 10 | - |
dc.language | 영어 | - |
dc.language.iso | ENG | - |
dc.publisher | Springer | - |
dc.title | Density based Fuzzy Support Vector Machines for Multicategory Pattern Classification | - |
dc.type | Article | - |
dc.publisher.location | 영국 | - |
dc.identifier.doi | 10.1007/978-3-540-72432-2_12 | - |
dc.identifier.scopusid | 2-s2.0-59549107689 | - |
dc.identifier.wosid | 000249852400012 | - |
dc.identifier.bibliographicCitation | Analysis and Design of Intelligent Systems using Soft Computing Techniques, v.41, pp 109 - 118 | - |
dc.citation.title | Analysis and Design of Intelligent Systems using Soft Computing Techniques | - |
dc.citation.volume | 41 | - |
dc.citation.startPage | 109 | - |
dc.citation.endPage | 118 | - |
dc.description.isOpenAccess | N | - |
dc.description.journalRegisteredClass | scopus | - |
dc.subject.keywordAuthor | Density | - |
dc.subject.keywordAuthor | Multiclass problems | - |
dc.subject.keywordAuthor | Membership functions | - |
dc.subject.keywordAuthor | FSVM | - |
dc.identifier.url | https://link.springer.com/chapter/10.1007/978-3-540-72432-2_12 | - |
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.
55 Hanyangdeahak-ro, Sangnok-gu, Ansan, Gyeonggi-do, 15588, Korea+82-31-400-4269 sweetbrain@hanyang.ac.kr
COPYRIGHT © 2021 HANYANG 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.