New multivariate kernel density estimator for uncertain data classification
- Title
- New multivariate kernel density estimator for uncertain data classification
- Author
- 김병훈
- Keywords
- Uncertain classifcation; Kernel density estimator; Bayesian classifer; Semiconductor DRAM
- Issue Date
- 2021-08
- Publisher
- SPRINGER
- Citation
- ANNALS OF OPERATIONS RESEARCH, v. 303, Page. 413-431
- 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.
- URI
- https://link.springer.com/article/10.1007/s10479-020-03715-4https://repository.hanyang.ac.kr/handle/20.500.11754/168991
- ISSN
- 0254-5330
- DOI
- 10.1007/s10479-020-03715-4
- Appears in Collections:
- COLLEGE OF ENGINEERING SCIENCES[E](공학대학) > INDUSTRIAL AND MANAGEMENT ENGINEERING(산업경영공학과) > Articles
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