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Kernel Rotation Forests for Classification
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Shim, Jaewoong | - |
dc.contributor.author | Kang, Seokho | - |
dc.contributor.author | Cho, Sungzoon | - |
dc.date.accessioned | 2022-10-19T00:31:17Z | - |
dc.date.available | 2022-10-19T00:31:17Z | - |
dc.date.created | 2022-10-12 | - |
dc.date.issued | 2020-02 | - |
dc.identifier.citation | 2020 IEEE INTERNATIONAL CONFERENCE ON BIG DATA AND SMART COMPUTING (BIGCOMP 2020), pp.406-409 | - |
dc.identifier.issn | 2375-933X | - |
dc.identifier.uri | https://hdl.handle.net/10371/186450 | - |
dc.description.abstract | There have been significant research efforts for developing decision tree (DT)-based ensemble methods. Such methods generally construct an ensemble by aggregating a large number of unpruned DTs, thereby yielding good classification accuracy. A recently developed method, rotation forest, is known to achieve better classification accuracy by rotating the dataset using principal component analysis (PCA). This paper describes a new method called kernel rotation forest, which is an extension of rotation forest. The proposed method applies kernel PCA instead of linear PCA to extract non-linear features when training DTs. Experimental results showed that kernel rotation forest outperforms rotation forest as well as other DT-based ensemble methods. | - |
dc.language | 영어 | - |
dc.publisher | IEEE | - |
dc.title | Kernel Rotation Forests for Classification | - |
dc.type | Article | - |
dc.identifier.doi | 10.1109/BigComp48618.2020.00-40 | - |
dc.citation.journaltitle | 2020 IEEE INTERNATIONAL CONFERENCE ON BIG DATA AND SMART COMPUTING (BIGCOMP 2020) | - |
dc.identifier.wosid | 000569987500070 | - |
dc.identifier.scopusid | 2-s2.0-85084366156 | - |
dc.citation.endpage | 409 | - |
dc.citation.startpage | 406 | - |
dc.description.isOpenAccess | N | - |
dc.contributor.affiliatedAuthor | Cho, Sungzoon | - |
dc.type.docType | Proceedings Paper | - |
dc.description.journalClass | 1 | - |
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