Naive Bayes classifiers boosted by sufficient dimension reduction: applications to top-k classification
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Yang, S.H. | - |
dc.contributor.author | Shin, S.J. | - |
dc.contributor.author | Sung, W. | - |
dc.contributor.author | Lee, C.W. | - |
dc.date.accessioned | 2022-12-11T08:40:30Z | - |
dc.date.available | 2022-12-11T08:40:30Z | - |
dc.date.created | 2022-12-08 | - |
dc.date.issued | 2022 | - |
dc.identifier.issn | 2287-7843 | - |
dc.identifier.uri | https://scholar.korea.ac.kr/handle/2021.sw.korea/146962 | - |
dc.description.abstract | The naive Bayes classifier is one of the most straightforward classification tools and directly estimates the class probability. However, because it relies on the independent assumption of the predictor, which is rarely satisfied in real-world problems, its application is limited in practice. In this article, we propose employing sufficient dimension reduction (SDR) to substantially improve the performance of the naive Bayes classifier, which is often deteriorated when the number of predictors is not restrictively small. This is not surprising as SDR reduces the predictor dimension without sacrificing classification information, and predictors in the reduced space are constructed to be uncorrelated. Therefore, SDR leads the naive Bayes to no longer be naive. We applied the proposed naive Bayes classifier after SDR to build a recommendation system for the eyewear-frames based on customers’ face shape, demonstrating its utility in the top-k classification problem. © 2022 The Korean Statistical Society, and Korean International Statistical Society. All rights reserved. | - |
dc.language | English | - |
dc.language.iso | en | - |
dc.publisher | Korean Statistical Society | - |
dc.title | Naive Bayes classifiers boosted by sufficient dimension reduction: applications to top-k classification | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Shin, S.J. | - |
dc.identifier.doi | 10.29220/CSAM.2022.29.5.603 | - |
dc.identifier.scopusid | 2-s2.0-85141320829 | - |
dc.identifier.bibliographicCitation | Communications for Statistical Applications and Methods, v.29, no.5, pp.603 - 614 | - |
dc.relation.isPartOf | Communications for Statistical Applications and Methods | - |
dc.citation.title | Communications for Statistical Applications and Methods | - |
dc.citation.volume | 29 | - |
dc.citation.number | 5 | - |
dc.citation.startPage | 603 | - |
dc.citation.endPage | 614 | - |
dc.type.rims | ART | - |
dc.type.docType | Article | - |
dc.identifier.kciid | ART002880730 | - |
dc.description.journalClass | 1 | - |
dc.description.isOpenAccess | Y | - |
dc.description.journalRegisteredClass | scopus | - |
dc.description.journalRegisteredClass | kci | - |
dc.description.journalRegisteredClass | other | - |
dc.subject.keywordAuthor | Dimension reduction | - |
dc.subject.keywordAuthor | Recommendation system | - |
dc.subject.keywordAuthor | Soft classification | - |
dc.subject.keywordAuthor | Top-k classification | - |
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.
145 Anam-ro, Seongbuk-gu, Seoul, 02841, Korea+82-2-3290-2963
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.