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Seoung Bum Kim
Researcher at Korea University
Publications - 197
Citations - 2957
Seoung Bum Kim is an academic researcher from Korea University. The author has contributed to research in topics: Computer science & Control chart. The author has an hindex of 26, co-authored 165 publications receiving 2260 citations. Previous affiliations of Seoung Bum Kim include University of Texas at Arlington & Center for Discrete Mathematics and Theoretical Computer Science.
Papers
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Journal ArticleDOI
A review and analysis of the Mahalanobis-Taguchi system
William H. Woodall,Rachelle Koudelik,Kwok-Leung Tsui,Seoung Bum Kim,Zachary G. Stoumbos,Christos P. Carvounis +5 more
TL;DR: The Mahalanobis-Taguchi system (MTS) as mentioned in this paper is a relatively new collection of methods proposed for diagnosis and forecasting using multivariate data, which is used to measure the level of abnormality of abnormal items compared to a group of normal items.
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One-class classification-based control charts for multivariate process monitoring
TL;DR: Attempts are made to extend the scope of application of the one-class classification technique to Statistical Process Control (SPC) problems by proposing new multivariate control charts that apply the effectiveness of one- class classification to improvement of Phase I and Phase II analysis in SPC.
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Content-based filtering for recommendation systems using multiattribute networks
Jieun Son,Seoung Bum Kim +1 more
TL;DR: This study proposes a novel CBF method that uses a multiattribute network to effectively reflect several attributes when calculating correlations to recommend items to users, and finds that this approach outperformed existing methods in terms of accuracy and robustness.
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Process monitoring using variational autoencoder for high-dimensional nonlinear processes
TL;DR: This study proposes a variational autoencoder (VAE) as a monitoring method that can address both nonlinear and nonnormal situations in high-dimensional processes and shows that VAE is appropriate for T 2 charts because it causes the reduced space to follow a multivariate normal distribution.
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Sequential random k-nearest neighbor feature selection for high-dimensional data
Chan Hee Park,Seoung Bum Kim +1 more
TL;DR: The study proposes the ensemble-based feature selection algorithm based on newly designed nearest-neighbor ensemble classifiers that finds significant features by using an iterative procedure and demonstrates the effectiveness and robustness of the proposed method.