Approximating support vector machine with artificial neural network for fast prediction. - Seokho Kang 001; Sungzoon Cho (Expert Syst. Appl., 2014)

Knowledge discovery in inspection reports of marine structures. - Seung-kyung Lee; Bongseok Kim; Minhoe Huh; Jooseoung Park; Seokho Kang 001; Sungzoon Cho; Dongha Lee 009; Daehyung Lee (Expert Syst. Appl., 2014)

Multi-class classification via heterogeneous ensemble of one-class classifiers. - Seokho Kang 001; Sungzoon Cho; Pilsung Kang 001 (Eng. Appl. Artif. Intell., 2015)

An efficient and effective ensemble of support vector machines for anti-diabetic drug failure prediction. - Seokho Kang 001; Pilsung Kang 001; Taehoon Ko; Sungzoon Cho; Su-jin Rhee; Kyung-Sang Yu (Expert Syst. Appl., 2015)

A novel multi-class classification algorithm based on one-class support vector machine. - Seokho Kang 001; Sungzoon Cho (Intell. Data Anal., 2015)

Optimal construction of one-against-one classifier based on meta-learning. - Seokho Kang 001; Sungzoon Cho (Neurocomputing, 2015)

Constructing a multi-class classifier using one-against-one approach with different binary classifiers. - Seokho Kang 001; Sungzoon Cho; Pilsung Kang 001 (Neurocomputing, 2015)

Improvement of virtual metrology performance by removing metrology noises in a training dataset. - Dongil Kim; Pilsung Kang 001; Seung-kyung Lee; Seokho Kang 001; Seungyong Doh; Sungzoon Cho (Pattern Anal. Appl., 2015)

Mining the relationship between production and customer service data for failure analysis of industrial products. - Seokho Kang 001; Eunji Kim 001; Jaewoong Shim; Sungzoon Cho; Wonsang Chang; Junhwan Kim (Comput. Ind. Eng., 2017)

Virtual metrology for copper-clad laminate manufacturing. - Misuk Kim; Seokho Kang 001; Jehyuk Lee; Hyunchang Cho; Sungzoon Cho; Jee Su Park (Comput. Ind. Eng., 2017)

Ranking process parameter association with low yield wafers using spec-out event network analysis. - Jiwon Yang; Seung-kyung Lee; Seokho Kang 001; Sungzoon Cho; Young-Hak Lee; Hae-Sang Park (Comput. Ind. Eng., 2017)

Reliable prediction of anti-diabetic drug failure using a reject option. - Seokho Kang 001; Sungzoon Cho; Su-jin Rhee; Kyung-Sang Yu (Pattern Anal. Appl., 2017)

Personalized prediction of drug efficacy for diabetes treatment via patient-level sequential modeling with neural networks. - Seokho Kang 001 (Artif. Intell. Medicine, 2018)

Regression with re-labeling for noisy data. - Youngdoo Son; Seokho Kang 001 (Expert Syst. Appl., 2018)

Product failure prediction with missing data. - Seokho Kang 001; Eunji Kim 001; Jaewoong Shim; Wonsang Chang; Sungzoon Cho (Int. J. Prod. Res., 2018)

Locally linear ensemble for regression. - Seokho Kang 001; Pilsung Kang 001 (Inf. Sci., 2018)

Approximate training of one-class support vector machines using expected margin. - Seokho Kang 001; Dongil Kim; Sungzoon Cho (Comput. Ind. Eng., 2019)

Molecular geometry prediction using a deep generative graph neural network. - Elman Mansimov; Omar Mahmood; Seokho Kang 001; Kyunghyun Cho (CoRR, 2019)

Rapid fault cause identification in surface mount technology processes based on factory-wide data analysis. - Dongil Kim; Jeongin Koo; Hyein Kim; Seokho Kang 001; Sang-Hyun Lee; Jeong-Tae Kang (Int. J. Distributed Sens. Networks, 2019)

Efficient learning of non-autoregressive graph variational autoencoders for molecular graph generation. - Youngchun Kwon; Jiho Yoo; Youn-Suk Choi; Wonjoon Song; Dongseon Lee; Seokho Kang 001 (J. Cheminformatics, 2019)

Conditional Molecular Design with Deep Generative Models. - Seokho Kang 001; Kyunghyun Cho (J. Chem. Inf. Model., 2019)

Clustering-based proxy measure for optimizing one-class classifiers. - Jaehong Yu; Seokho Kang 001 (Pattern Recognit. Lett., 2019)

Kernel Rotation Forests for Classification. - Jaewoong Shim; Seokho Kang 001; Sungzoon Cho (BigComp, 2020)

Unsupervised Anomaly Detection Using Style Distillation. - Hwehee Chung; Jongho Park; Jongsoo Keum; Hongdo Ki; Seokho Kang 001 (IEEE Access, 2020)

Rotation-Invariant Wafer Map Pattern Classification With Convolutional Neural Networks. - Seokho Kang 001 (IEEE Access, 2020)

Model validation failure in class imbalance problems. - Seokho Kang 001 (Expert Syst. Appl., 2020)

Expected margin-based pattern selection for support vector machines. - Dongil Kim; Seokho Kang 001; Sungzoon Cho (Expert Syst. Appl., 2020)

Compressed graph representation for scalable molecular graph generation. - Youngchun Kwon; Dongseon Lee; Youn-Suk Choi; Kyoham Shin; Seokho Kang 001 (J. Cheminformatics, 2020)

Predictive Modeling of NMR Chemical Shifts without Using Atomic-Level Annotations. - Seokho Kang 001; Youngchun Kwon; Dongseon Lee; Youn-Suk Choi (J. Chem. Inf. Model., 2020)

Neural Message Passing for NMR Chemical Shift Prediction. - Youngchun Kwon; Dongseon Lee; Youn-Suk Choi; Myeonginn Kang; Seokho Kang 001 (J. Chem. Inf. Model., 2020)

Joint modeling of classification and regression for improving faulty wafer detection in semiconductor manufacturing. - Seokho Kang 001 (J. Intell. Manuf., 2020)

Model-Agnostic Post-Processing Based on Recursive Feedback for Medical Image Segmentation. - Jaeho Kim; Seokho Kang 001 (IEEE Access, 2021)

A stacking ensemble classifier with handcrafted and convolutional features for wafer map pattern classification. - Hyungu Kang; Seokho Kang 001 (Comput. Ind., 2021)

Data-free knowledge distillation in neural networks for regression. - Myeonginn Kang; Seokho Kang 001 (Expert Syst. Appl., 2021)

Active cluster annotation for wafer map pattern classification in semiconductor manufacturing. - Jaewoong Shim; Seokho Kang 001; Sungzoon Cho (Expert Syst. Appl., 2021)

MI-MOTE: Multiple imputation-based minority oversampling technique for imbalanced and incomplete data classification. - Kyoham Shin; Jongmin Han; Seokho Kang 001 (Inf. Sci., 2021)

Active learning with missing values considering imputation uncertainty. - Jongmin Han; Seokho Kang 001 (Knowl. Based Syst., 2021)

Product failure prediction with missing data using graph neural networks. - Seokho Kang 001 (Neural Comput. Appl., 2021)

Semi-automatic wafer map pattern classification with convolutional neural networks. - Suhee Yoon; Seokho Kang 001 (Comput. Ind. Eng., 2022)

Domain-adaptive active learning for cost-effective virtual metrology modeling. - Jaewoong Shim; Seokho Kang 001 (Comput. Ind., 2022)

Dynamic imputation for improved training of neural network with missing values. - Jongmin Han; Seokho Kang 001 (Expert Syst. Appl., 2022)

Using binary classifiers for one-class classification. - Seokho Kang 001 (Expert Syst. Appl., 2022)

ADANOISE: Training neural networks with adaptive noise for imbalanced data classification. - Kyoham Shin; Seokho Kang 001 (Expert Syst. Appl., 2022)

Uncertainty-aware prediction of chemical reaction yields with graph neural networks. - Youngchun Kwon; Dongseon Lee; Youn-Suk Choi; Seokho Kang 001 (J. Cheminformatics, 2022)

Generative Modeling to Predict Multiple Suitable Conditions for Chemical Reactions. - Youngchun Kwon; Sun Kim; Youn-Suk Choi; Seokho Kang 001 (J. Chem. Inf. Model., 2022)

Class-Adaptive Data Augmentation for Image Classification. - Jisu Yoo; Seokho Kang 001 (IEEE Access, 2023)

Efficient improvement of classification accuracy via selective test-time augmentation. - Jongwook Son; Seokho Kang 001 (Inf. Sci., 2023)

Semi-supervised rotation-invariant representation learning for wafer map pattern analysis. - Hyungu Kang; Seokho Kang 001 (Eng. Appl. Artif. Intell., 2023)

Surrogate approach to uncertainty quantification of neural networks for regression. - Myeonginn Kang; Seokho Kang 001 (Appl. Soft Comput., 2023)

Supervised contrastive learning for wafer map pattern classification. - Youngjae Bae; Seokho Kang 001 (Eng. Appl. Artif. Intell., 2023)

Optimization of missing value imputation for neural networks. - Jongmin Han; Seokho Kang 001 (Inf. Sci., 2023)

Learning from single-defect wafer maps to classify mixed-defect wafer maps. - Jaewoong Shim; Seokho Kang 001 (Expert Syst. Appl., 2023)

Mixup Your Own Latent: Efficient and Robust Self-Supervised Learning on Small Images. - Eugene Yang; Hao Chen; Seokho Kang 001 (ECAI, 2024)

Photovoltaic Cell Defect Detection Based on Weakly Supervised Learning With Module-Level Annotations. - Hyungu Kang; Jeongmin Hong; Jiwon Lee; Seokho Kang 001 (IEEE Access, 2024)

Dynamic ensemble of regression neural networks based on predictive uncertainty. - Yoonhyung Lee; Seokho Kang 001 (Comput. Ind. Eng., 2024)

Knowledge distillation with insufficient training data for regression. - Myeonginn Kang; Seokho Kang 001 (Eng. Appl. Artif. Intell., 2024)

Training-free approach to constructing ensemble of local experts. - Sunbin Lee; Seokho Kang 001 (Expert Syst. Appl., 2024)

Score distillation for anomaly detection. - Jeongmin Hong; Seokho Kang 001 (Knowl. Based Syst., 2024)

Improving chemical reaction yield prediction using pre-trained graph neural networks. - Jongmin Han; Youngchun Kwon; Youn-Suk Choi; Seokho Kang 001 (J. Cheminformatics, 2024)

Why does this query need to be labeled?: Enhancing active learning through explanation-based interventions in query selection. - Jaewoong Shim; Seokho Kang 001 (Expert Syst. Appl., 2025)

Enhancing chemical reaction search through contrastive representation learning and human-in-the-loop. - Youngchun Kwon; Hyunjeong Jeon; Joonhyuk Choi; Youn-Suk Choi; Seokho Kang 001 (J. Cheminformatics, 2025)

Consistency-regularized graph neural networks for molecular property prediction. - Jongmin Han; Seokho Kang 001 (Neural Networks, 2026)