A KERNEL FISHER DISCRIMINANT ANALYSIS-BASED TREE ENSEMBLE CLASSIFIER: KFDA FOREST
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Kim, Donghwan | - |
dc.contributor.author | Park, Seung Hwan | - |
dc.contributor.author | Baek, Jun-Geol | - |
dc.date.accessioned | 2021-09-02T21:27:58Z | - |
dc.date.available | 2021-09-02T21:27:58Z | - |
dc.date.created | 2021-06-16 | - |
dc.date.issued | 2018 | - |
dc.identifier.issn | 1072-4761 | - |
dc.identifier.uri | https://scholar.korea.ac.kr/handle/2021.sw.korea/81050 | - |
dc.description.abstract | In general, an ensemble classifier is more accurate than a single classifier. In this study, we propose an ensemble classifier called the kernel Fisher discriminant analysis forest (KFDA Forest), which is a tree-based ensemble method that applies KFDA. To promote diversity, bootstrap is used, and variable sets are randomly divided into K subsets. KFDA is performed on each subset to increase classification accuracy. KFDA maximizes the distance between classes while minimizing the distance within classes. KFDA can also be applied to classification problems in a nonlinear data structure using the kernel trick because it can transform the input space into a kernel feature space, commonly named a rotation, rather than performing a dimensionality reduction. Because new feature axes and KFDA projections are parallel, decision trees are used as a base classifier. To compare the proposed method with existing ensemble methods, we apply these to real datasets from the UCI and KEEL repositories. | - |
dc.language | English | - |
dc.language.iso | en | - |
dc.publisher | UNIV CINCINNATI INDUSTRIAL ENGINEERING | - |
dc.title | A KERNEL FISHER DISCRIMINANT ANALYSIS-BASED TREE ENSEMBLE CLASSIFIER: KFDA FOREST | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Baek, Jun-Geol | - |
dc.identifier.scopusid | 2-s2.0-85060724625 | - |
dc.identifier.wosid | 000456069800002 | - |
dc.identifier.bibliographicCitation | INTERNATIONAL JOURNAL OF INDUSTRIAL ENGINEERING-THEORY APPLICATIONS AND PRACTICE, v.25, no.5, pp.569 - 579 | - |
dc.relation.isPartOf | INTERNATIONAL JOURNAL OF INDUSTRIAL ENGINEERING-THEORY APPLICATIONS AND PRACTICE | - |
dc.citation.title | INTERNATIONAL JOURNAL OF INDUSTRIAL ENGINEERING-THEORY APPLICATIONS AND PRACTICE | - |
dc.citation.volume | 25 | - |
dc.citation.number | 5 | - |
dc.citation.startPage | 569 | - |
dc.citation.endPage | 579 | - |
dc.type.rims | ART | - |
dc.type.docType | Article; Proceedings Paper | - |
dc.description.journalClass | 1 | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Engineering | - |
dc.relation.journalWebOfScienceCategory | Engineering, Industrial | - |
dc.relation.journalWebOfScienceCategory | Engineering, Manufacturing | - |
dc.subject.keywordAuthor | classification | - |
dc.subject.keywordAuthor | ensemble classifier | - |
dc.subject.keywordAuthor | decision trees | - |
dc.subject.keywordAuthor | kernel fisher discriminant analysis | - |
dc.subject.keywordAuthor | rotation forest | - |
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.
(02841) 서울특별시 성북구 안암로 14502-3290-1114
COPYRIGHT © 2021 Korea 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.