Principal weighted logistic regression for sufficient dimension reduction in binary classification
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Kim, Boyoung | - |
dc.contributor.author | Shin, Seung Jun | - |
dc.date.accessioned | 2021-09-01T14:02:24Z | - |
dc.date.available | 2021-09-01T14:02:24Z | - |
dc.date.created | 2021-06-19 | - |
dc.date.issued | 2019-06 | - |
dc.identifier.issn | 1226-3192 | - |
dc.identifier.uri | https://scholar.korea.ac.kr/handle/2021.sw.korea/64878 | - |
dc.description.abstract | Sufficient dimension reduction (SDR) is a popular supervised machine learning technique that reduces the predictor dimension and facilitates subsequent data analysis in practice. In this article, we propose principal weighted logistic regression (PWLR), an efficient SDR method in binary classification where inverse-regression-based SDR methods often suffer. We first develop linear PWLR for linear SDR and study its asymptotic properties. We then extend it to nonlinear SDR and propose the kernel PWLR. Evaluations with both simulated and real data show the promising performance of the PWLR for SDR in binary classification. (C) 2018 The Korean Statistical Society. Published by Elsevier B.V. All rights reserved. | - |
dc.language | English | - |
dc.language.iso | en | - |
dc.publisher | KOREAN STATISTICAL SOC | - |
dc.subject | SLICED INVERSE REGRESSION | - |
dc.subject | CENTRAL SUBSPACE | - |
dc.title | Principal weighted logistic regression for sufficient dimension reduction in binary classification | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Shin, Seung Jun | - |
dc.identifier.doi | 10.1016/j.jkss.2018.11.001 | - |
dc.identifier.scopusid | 2-s2.0-85058181620 | - |
dc.identifier.wosid | 000468713300003 | - |
dc.identifier.bibliographicCitation | JOURNAL OF THE KOREAN STATISTICAL SOCIETY, v.48, no.2, pp.194 - 206 | - |
dc.relation.isPartOf | JOURNAL OF THE KOREAN STATISTICAL SOCIETY | - |
dc.citation.title | JOURNAL OF THE KOREAN STATISTICAL SOCIETY | - |
dc.citation.volume | 48 | - |
dc.citation.number | 2 | - |
dc.citation.startPage | 194 | - |
dc.citation.endPage | 206 | - |
dc.type.rims | ART | - |
dc.type.docType | Article | - |
dc.identifier.kciid | ART002482642 | - |
dc.description.journalClass | 1 | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.description.journalRegisteredClass | kci | - |
dc.relation.journalResearchArea | Mathematics | - |
dc.relation.journalWebOfScienceCategory | Statistics & Probability | - |
dc.subject.keywordPlus | SLICED INVERSE REGRESSION | - |
dc.subject.keywordPlus | CENTRAL SUBSPACE | - |
dc.subject.keywordAuthor | Binary classification | - |
dc.subject.keywordAuthor | Model-free feature extraction | - |
dc.subject.keywordAuthor | Weighted logistic regression | - |
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