Locally linear ensemble for regression
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Kang, Seokho | - |
dc.contributor.author | Kang, Pilsung | - |
dc.date.accessioned | 2021-09-02T14:05:12Z | - |
dc.date.available | 2021-09-02T14:05:12Z | - |
dc.date.created | 2021-06-16 | - |
dc.date.issued | 2018-03 | - |
dc.identifier.issn | 0020-0255 | - |
dc.identifier.uri | https://scholar.korea.ac.kr/handle/2021.sw.korea/76861 | - |
dc.description.abstract | Considerable research effort has been dedicated to the development of prediction models for yielding greater prediction accuracy in regression problems. Although non-linear models have achieved superior prediction accuracy by addressing the non-linearity of complex data, linear models are still favored because of their high prediction speed. In this study, a locally linear ensemble regression (LLER) is proposed in order to effectively address non-linearity while maintaining the advantage of linear models. The LLER predicts new instances based on multiple linear models that are trained on the regions that identify the local linearity of data. To achieve this, data are decomposed into several locally linear regions based on an expectation-maximization procedure, and linear models are built as local experts for each region to constitute an ensemble. We demonstrate the effectiveness of the LLER through experimental validation with benchmark datasets. (C) 2017 Elsevier Inc. All rights reserved. | - |
dc.language | English | - |
dc.language.iso | en | - |
dc.publisher | ELSEVIER SCIENCE INC | - |
dc.subject | DYNAMIC CLASSIFIER SELECTION | - |
dc.subject | NEURAL-NETWORK | - |
dc.subject | MODEL | - |
dc.title | Locally linear ensemble for regression | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Kang, Pilsung | - |
dc.identifier.doi | 10.1016/j.ins.2017.12.022 | - |
dc.identifier.scopusid | 2-s2.0-85038021510 | - |
dc.identifier.wosid | 000424188400012 | - |
dc.identifier.bibliographicCitation | INFORMATION SCIENCES, v.432, pp.199 - 209 | - |
dc.relation.isPartOf | INFORMATION SCIENCES | - |
dc.citation.title | INFORMATION SCIENCES | - |
dc.citation.volume | 432 | - |
dc.citation.startPage | 199 | - |
dc.citation.endPage | 209 | - |
dc.type.rims | ART | - |
dc.type.docType | Article | - |
dc.description.journalClass | 1 | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Computer Science | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Information Systems | - |
dc.subject.keywordPlus | DYNAMIC CLASSIFIER SELECTION | - |
dc.subject.keywordPlus | NEURAL-NETWORK | - |
dc.subject.keywordPlus | MODEL | - |
dc.subject.keywordAuthor | Regression | - |
dc.subject.keywordAuthor | Locally linear ensemble | - |
dc.subject.keywordAuthor | Linear model | - |
dc.subject.keywordAuthor | Ensemble learning | - |
dc.subject.keywordAuthor | Local expert | - |
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.