Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | 김병훈 | - |
dc.date.accessioned | 2022-03-11T02:33:06Z | - |
dc.date.available | 2022-03-11T02:33:06Z | - |
dc.date.issued | 2021-08 | - |
dc.identifier.citation | ANNALS OF OPERATIONS RESEARCH, v. 303, Page. 413-431 | en_US |
dc.identifier.issn | 0254-5330 | - |
dc.identifier.uri | https://link.springer.com/article/10.1007/s10479-020-03715-4 | - |
dc.identifier.uri | https://repository.hanyang.ac.kr/handle/20.500.11754/168991 | - |
dc.description.abstract | Uncertainty in data occurs in diverse applications due to measurement errors, data incompleteness, and multiple repeated measurements. Several classifers for uncertain data have been developed to tackle this uncertainty. However, the existing classifers do not consider the dependencies among uncertain features, even though this dependency has a critical efect on classifcation accuracy. Therefore, we propose a new Bayesian classifcation 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 classifcation of uncertain data in terms of classifcation accuracy. | en_US |
dc.description.sponsorship | Part of this work was supported by the Korea Institute for Advancement of Technology grant funded by the Korea Government (Grant No.: P0008691, HRD Program for Industrial Innovation) and by the research fund of the National Research Foundation of Korea (Grant No.: NRF-2019R1F1A1042307). We thank the anonymous reviewers whose comments and suggestions helped improve and clarify this manuscript. | en_US |
dc.language.iso | en | en_US |
dc.publisher | SPRINGER | en_US |
dc.subject | Uncertain classifcation | en_US |
dc.subject | Kernel density estimator | en_US |
dc.subject | Bayesian classifer | en_US |
dc.subject | Semiconductor DRAM | en_US |
dc.title | New multivariate kernel density estimator for uncertain data classification | en_US |
dc.type | Article | en_US |
dc.relation.volume | 303 | - |
dc.identifier.doi | 10.1007/s10479-020-03715-4 | - |
dc.relation.page | 413-431 | - |
dc.relation.journal | ANNALS OF OPERATIONS RESEARCH | - |
dc.contributor.googleauthor | Kim, Byunghoon | - |
dc.contributor.googleauthor | Jeong, Young-Seon | - |
dc.contributor.googleauthor | Jeong, Myong K. | - |
dc.relation.code | 2021003206 | - |
dc.sector.campus | E | - |
dc.sector.daehak | COLLEGE OF ENGINEERING SCIENCES[E] | - |
dc.sector.department | DEPARTMENT OF INDUSTRIAL AND MANAGEMENT ENGINEERING | - |
dc.identifier.pid | byungkim | - |
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.