New multivariate kernel density estimator for uncertain data classification
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Kim, Byunghoon | - |
dc.contributor.author | Jeong, Young-Seon | - |
dc.contributor.author | Jeong, Myong K. | - |
dc.date.accessioned | 2021-06-22T06:00:25Z | - |
dc.date.available | 2021-06-22T06:00:25Z | - |
dc.date.issued | 2021-08 | - |
dc.identifier.issn | 0254-5330 | - |
dc.identifier.issn | 1572-9338 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/erica/handle/2021.sw.erica/931 | - |
dc.description.abstract | Uncertainty in data occurs in diverse applications due to measurement errors, data incompleteness, and multiple repeated measurements. Several classifiers for uncertain data have been developed to tackle this uncertainty. However, the existing classifiers do not consider the dependencies among uncertain features, even though this dependency has a critical effect on classification accuracy. Therefore, we propose a new Bayesian classification model that considers the correlation among uncertain features. To handle the uncertainty of data, new multivariate kernel density estimators are developed to estimate the class conditional probability density function of categorical, continuous, and mixed uncertain data. Experimental results with simulated data and real-life data sets show that the proposed approach is better than the existing approaches for classification of uncertain data in terms of classification accuracy. | - |
dc.format.extent | 19 | - |
dc.language | 영어 | - |
dc.language.iso | ENG | - |
dc.publisher | SPRINGER | - |
dc.title | New multivariate kernel density estimator for uncertain data classification | - |
dc.type | Article | - |
dc.publisher.location | 네델란드 | - |
dc.identifier.doi | 10.1007/s10479-020-03715-4 | - |
dc.identifier.scopusid | 2-s2.0-85089828066 | - |
dc.identifier.wosid | 000562703600001 | - |
dc.identifier.bibliographicCitation | ANNALS OF OPERATIONS RESEARCH, v.303, no.1-2, pp 413 - 431 | - |
dc.citation.title | ANNALS OF OPERATIONS RESEARCH | - |
dc.citation.volume | 303 | - |
dc.citation.number | 1-2 | - |
dc.citation.startPage | 413 | - |
dc.citation.endPage | 431 | - |
dc.type.docType | Article; Early Access | - |
dc.description.isOpenAccess | N | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Operations Research & Management Science | - |
dc.relation.journalWebOfScienceCategory | Operations Research & Management Science | - |
dc.subject.keywordAuthor | Uncertain classification | - |
dc.subject.keywordAuthor | Kernel density estimator | - |
dc.subject.keywordAuthor | Bayesian classifier | - |
dc.subject.keywordAuthor | Semiconductor DRAM | - |
dc.identifier.url | https://link.springer.com/article/10.1007/s10479-020-03715-4 | - |
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.
55 Hanyangdeahak-ro, Sangnok-gu, Ansan, Gyeonggi-do, 15588, Korea+82-31-400-4269 sweetbrain@hanyang.ac.kr
COPYRIGHT © 2021 HANYANG 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.