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Kernel Rotation Forests for Classification

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dc.contributor.authorShim, Jaewoong-
dc.contributor.authorKang, Seokho-
dc.contributor.authorCho, Sungzoon-
dc.date.accessioned2022-10-19T00:31:17Z-
dc.date.available2022-10-19T00:31:17Z-
dc.date.created2022-10-12-
dc.date.issued2020-02-
dc.identifier.citation2020 IEEE INTERNATIONAL CONFERENCE ON BIG DATA AND SMART COMPUTING (BIGCOMP 2020), pp.406-409-
dc.identifier.issn2375-933X-
dc.identifier.urihttps://hdl.handle.net/10371/186450-
dc.description.abstractThere 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.language영어-
dc.publisherIEEE-
dc.titleKernel Rotation Forests for Classification-
dc.typeArticle-
dc.identifier.doi10.1109/BigComp48618.2020.00-40-
dc.citation.journaltitle2020 IEEE INTERNATIONAL CONFERENCE ON BIG DATA AND SMART COMPUTING (BIGCOMP 2020)-
dc.identifier.wosid000569987500070-
dc.identifier.scopusid2-s2.0-85084366156-
dc.citation.endpage409-
dc.citation.startpage406-
dc.description.isOpenAccessN-
dc.contributor.affiliatedAuthorCho, Sungzoon-
dc.type.docTypeProceedings Paper-
dc.description.journalClass1-
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