M
Myong K. Jeong
Researcher at Rutgers University
Publications - 100
Citations - 2363
Myong K. Jeong is an academic researcher from Rutgers University. The author has contributed to research in topics: Feature selection & Support vector machine. The author has an hindex of 24, co-authored 95 publications receiving 1955 citations. Previous affiliations of Myong K. Jeong include Electronics and Telecommunications Research Institute & Center for Discrete Mathematics and Theoretical Computer Science.
Papers
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Weighted dynamic time warping for time series classification
TL;DR: A novel distance measure, called a weighted DTW (WDTW), which is a penalty-based DTW that penalizes points with higher phase difference between a reference point and a testing point in order to prevent minimum distance distortion caused by outliers is proposed.
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Automatic Identification of Defect Patterns in Semiconductor Wafer Maps Using Spatial Correlogram and Dynamic Time Warping
TL;DR: A new methodology in which spatial correlogram is used for the detection of the presence of spatial autocorrelations and for the classification of defect patterns on the wafer map and it is shown that the method is robust to random noise and has a robust performance regardless of defect location and size.
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AADT prediction using support vector regression with data-dependent parameters
TL;DR: A modified version of a pattern recognition technique known as support vector machine for regression (SVR) to forecast AADT is presented and the performance of the SVR-DP was compared with those of Holt exponential smoothing (Holt-ES) and of ordinary least-square linear regression (OLS-regression).
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Class dependent feature scaling method using naive Bayes classifier for text datamining
Eunseog Youn,Myong K. Jeong +1 more
TL;DR: A new feature scaling method, called class-dependent-feature-weighting (CDFW) using naive Bayes (NB) classifier, which combines CDFW and recursive feature elimination (RFE) and results showed that CDFW-NB-RFE outperformed other popular feature ranking schemes used on text datasets.
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Discovery of metabolite features for the modelling and analysis of high-resolution NMR spectra
Hyunwoo Cho,Seoung Bum Kim,Myong K. Jeong,Youngja Park,Nana Gletsu Miller,Thomas R. Ziegler,Dean P. Jones +6 more
TL;DR: This study presents three feature selection methods for identifying the metabolite features in nuclear magnetic resonance spectra that contribute to the distinction of samples among varying nutritional conditions and showed that the best classification was achieved from an OSC-PLS-DA model.