International Journals (†equal contribution, *corresponding author)

2024

1. Sunbin Lee, Seokho Kang* (2024), "Training-free approach to constructing ensemble of local experts", Expert Systems with Applications 257: 125072

2. Youngjae Bae, Kyunghye Nam, Seokho Kang* (2024), "CFD-ML: Stream-based active learning of computational fluid dynamics simulations for efficient product design", Computers in Industry 161: 104122.

3. Jeongmin Hong, Seokho Kang* (2024), "Score distillation for anomaly detection", Knowledge-Based Systems 295: 111842. [code]

4. Myeonginn Kang, Seokho Kang* (2024), "Knowledge distillation with insufficient training data for regression", Engineering Applications of Artificial Intelligence 132: 108001.

5. Yoonhyung Lee, Seokho Kang* (2024), "Dynamic ensemble of regression neural networks based on predictive uncertainty", Computers & Industrial Engineering 190: 110011.

6. Myeonginn Kang, Jongmin Han, Yangjoon Kim, Seongcheon Kim, Seokho Kang* (2024), "Data-driven autonomous operation of VOCs removal system", Scientific Reports 14: 5953.

7. Jongmin Han, Youngchun Kwon, Youn-Suk Choi*, Seokho Kang* (2024), "Improving chemical reaction yield prediction using pre-trained graph neural networks", Journal of Cheminformatics 16: 25. [code]

8. Hyungu Kang, Jeongmin Hong, Jiwon Lee, Seokho Kang* (2024), "Photovoltaic cell defect detection based on weakly supervised learning with module-level annotations", IEEE Access 12: 5575-5583.

2023

1. 1. Jaewoong Shim, Seokho Kang* (2023), "Learning from single-defect wafer maps to classify mixed-defect wafer maps", Expert Systems with Applications 233: 120923.

   2. Youngchun Kwon†, Hyukju Kwon†, Jongmin Han, Myeonginn Kang, Ji-Yeong Kim, Dongyeeb Shin, Youn-Suk Choi*, Seokho Kang* (2023), "Retention time prediction through learning from a small training data set with a pretrained graph neural network", Analytical Chemistry 95(47): 17273-17283. [code]

   3. Youngjae Bae, Seokho Kang* (2023), "Supervised contrastive learning for wafer map pattern classification", Engineering Applications of Artificial Intelligence 126: 107154. [code]

   4. Jongmin Han, Seokho Kang* (2023), "Optimization of missing value imputation for neural networks", Information Sciences 649: 119668.

   5. Jongwook Son, Seokho Kang* (2023), "Efficient improvement of classification accuracy via selective test-time augmentation", Information Sciences 642: 119148.

   6. Myeonginn Kang, Seokho Kang* (2023), "Surrogate approach to uncertainty quantification of neural networks for regression", Applied Soft Computing 139: 110234. [code]

   7. Hyungu Kang, Seokho Kang* (2023), "Semi-supervised rotation-invariant representation learning for wafer map pattern analysis", Engineering Applications of Artificial Intelligence 120: 105864.

   8. Jisu Yoo, Seokho Kang* (2023), "Class-adaptive data augmentation for image classification", IEEE Access 11: 26393-26402.

2022

1. Youngchun Kwon, Sun Kim, Youn-Suk Choi*, Seokho Kang* (2022), "Generative modeling to predict multiple suitable conditions for chemical reactions", Journal of Chemical Information and Modeling 62(23): 5952-5960. [code]

2. Jongmin Han†, Hyungu Kang†, Seokho Kang*, Youngchun Kwon, Dongseon Lee, Youn-Suk Choi* (2022), "Scalable graph neural network for NMR chemical shift prediction", Physical Chemistry Chemical Physics 24(43): 26870-26878. [code]

3. Jongmin Han, Seokho Kang* (2022), "Dynamic imputation for improved training of neural network with missing values", Expert Systems with Applications 194: 116508. [code]

4. Suhee Yoon, Seokho Kang* (2022), "Semi-automatic wafer map pattern classification with convolutional neural networks", Computers & Industrial Engineering 166: 107977.

5. Kyoham Shin, Seokho Kang* (2022), "ADANOISE: Training neural networks with adaptive noise for imbalanced data classification", Expert Systems with Applications 192: 116364.

6. Jaewoong Shim, Seokho Kang* (2022), "Domain-adaptive active learning for cost-effective virtual metrology modeling", Computers in Industry 135: 103572.

7. Youngchun Kwon, Dongseon Lee, Youn-Suk Choi*, Seokho Kang* (2022), "Uncertainty-aware prediction of chemical reaction yields with graph neural networks", Journal of Cheminformatics 14: 2. [code]

8. Seokho Kang* (2022), "Using binary classifiers for one-class classification", Expert Systems with Applications 187: 115920.

2021

1. Jaeho Kim, Seokho Kang* (2021), "Model-agnostic post-processing based on recursive feedback for medical image segmentation", IEEE Access 9: 157035-157042.

2. Jaewoong Shim, Seokho Kang, Sungzoon Cho* (2021), "Active cluster annotation for wafer map pattern classification in semiconductor manufacturing", Expert Systems with Applications 183: 115429.

3. Youngchun Kwon, Dongseon Lee, Youn-Suk Choi*, Seokho Kang* (2021), "Molecular search by NMR spectrum based on evaluation of matching between spectrum and molecule", Scientific Reports 11: 20998. [code]

4. Kyoham Shin, Jongmin Han, Seokho Kang* (2021), "MI-MOTE: Multiple imputation-based minority oversampling technique for imbalanced and incomplete data classification", Information Sciences 575: 80-89.

5. Jaewoong Shim, Seokho Kang, Sungzoon Cho* (2021), "Active inspection for cost-effective fault prediction in manufacturing process", Journal of Process Control 105: 250-258.

6. Youngchun Kwon†, Seokho Kang†, Youn-Suk Choi*, Inkoo Kim (2021), "Evolutionary design of molecules based on deep learning and a genetic algorithm", Scientific Reports 11: 17304.

7. Hyungu Kang, Seokho Kang* (2021), "A stacking ensemble classifier with handcrafted and convolutional features for wafer map pattern classification", Computers in Industry 129: 103450. [code]

8. Myeonginn Kang, Seokho Kang* (2021), "Data-free knowledge distillation in neural networks for regression", Expert Systems with Applications 175: 114813.

9. Jongmin Han, Seokho Kang* (2021), "Active learning with missing values considering imputation uncertainty", Knowledge-Based Systems 224: 107079. [code]

10. Seokho Kang* (2021), "Product failure prediction with missing data using graph neural networks", Neural Computing & Applications 33: 7225-7234.

11. Seokho Kang* (2021), "k-nearest neighbor learning with graph neural networks", Mathematics 9(8): 830.

2020

1. Hwehee Chung†, Jongho Park†, Jongsoo Keum†, Hongdo Ki*, Seokho Kang* (2020), "Unsupervised anomaly detection using style distillation", IEEE Access 8: 221494-221502.

2. Youngchun Kwon, Dongseon Lee, Youn-Suk Choi*, Kyoham Shin, Seokho Kang* (2020), "Compressed graph representation for scalable molecular graph generation", Journal of Cheminformatics 12: 58. [code]

3. Seokho Kang* (2020), "Rotation-invariant wafer map pattern classification with convolutional neural networks", IEEE Access 8: 170650-170658.

4. Seokho Kang, Youngchun Kwon, Dongseon Lee, Youn-Suk Choi* (2020), "Predictive modeling of NMR chemical shifts without using atomic-level annotations", Journal of Chemical Information and Modeling 60(8): 3765-3769. [code]

5. Jaewoong Shim, Seokho Kang, Sungzoon Cho* (2020), "Active learning of convolutional neural network for cost-effective wafer map pattern classification", IEEE Transactions on Semiconductor Manufacturing 33(2): 258-266.

6. Seokho Kang* (2020), "Model validation failure in class imbalance problems", Expert Systems with Applications 146: 113190.

7. Youngchun Kwon, Dongseon Lee, Youn-Suk Choi, Myeonginn Kang, Seokho Kang* (2020), "Neural message passing for NMR chemical shift prediction", Journal of Chemical Information and Modeling 60(4): 2024-2030. [code]

8. Seokho Kang* (2020), "Joint modeling of classification and regression for improving faulty wafer detection in semiconductor manufacturing", Journal of Intelligent Manufacturing 31(2): 319-326.

9. Dongil Kim, Seokho Kang*, Sungzoon Cho (2020), "Expected margin-based pattern selection for support vector machines", Expert Systems with Applications 139: 112865.

2019

1. Elman Mansimov, Omar Mahmood, Seokho Kang, Kyunghyun Cho* (2019), "Molecular geometry prediction using a deep generative graph neural network", Scientific Reports 9: 20381. [code]

2. Youngchun Kwon, Jiho Yoo, Youn-Suk Choi, Won-Joon Son, Dongseon Lee, Seokho Kang* (2019), "Efficient learning of non-autoregressive graph variational autoencoders for molecular graph generation", Journal of Cheminformatics 11: 70. [code]

3. Seokho Kang*, Daewoong An, Jaeyoung Rim (2019), "Incorporating virtual metrology into failure prediction", IEEE Transactions on Semiconductor Manufacturing 32(4): 553-558.

4. Dongil Kim, Seokho Kang* (2019), "Effect of irrelevant variables on faulty wafer detection in semiconductor manufacturing", Energies 12(13): 2530.

5. Seokho Kang, Dongil Kim*, Sungzoon Cho (2019), "Approximate training of one-class support vector machines using expected margin", Computers & Industrial Engineering 130: 772-778.

6. Dongil Kim, Jeongin Koo, Hyein Kim, Seokho Kang*, Sang Hyun Lee, Jeong Tae Kang (2019), "Rapid fault cause identification in surface mount technology processes based on factory-wide data analysis", International Journal of Distributed Sensor Networks 15(2): 1550147719832802.

7. Seokho Kang*, Kyunghyun Cho (2019), "Conditional molecular design with deep generative models", Journal of Chemical Information and Modeling 59(1): 43-52. [code]

8. Jaehong Yu, Seokho Kang* (2019), "Clustering-based proxy measure for optimizing one-class classifiers", Pattern Recognition Letters 117: 37-44.

2018

1. Youngdoo Son, Seokho Kang* (2018), "Regression with re-labeling for noisy data", Expert Systems with Applications 114: 578-587.

2. Kyungdoc Kim†, Seokho Kang†, Jiho Yoo, Youngchun Kwon, Youngmin Nam, Dongseon Lee, Inkoo Kim, Youn-Suk Choi*, Yongsik Jung, Sangmo Kim, Won-Joon Son, Jhunmo Son, Hyo Sug Lee*, Sunghan Kim*, Jaikwang Shin, Sungwoo Hwang (2018), "Deep learning-based inverse design model for intelligent discovery of organic molecules", npj Computational Materials 4: 67.

3. Seokho Kang, Eunji Kim, Jaewoong Shim, Wonsang Chang, Sungzoon Cho* (2018), "Product failure prediction with missing data", International Journal of Production Research 56(14): 4849-4859.

4. Seokho Kang* (2018), "Personalized prediction of drug efficacy for diabetes treatment via patient-level sequential modeling with neural networks", Artificial Intelligence in Medicine 85: 1-6.

5. Seokho Kang, Pilsung Kang* (2018), "Locally linear ensemble for regression", Information Sciences 432: 199-209.

6. Seokho Kang* (2018), "On effectiveness of transfer learning approach for neural network-based virtual metrology modeling", IEEE Transactions on Semiconductor Manufacturing 31(1): 149-155.

Prior to SKKU (~2017)

1. Jiwon Yang, Seung-kyung Lee, Seokho Kang, Sungzoon Cho*, Young-Hak Lee, Hae-Sang Park (2017), "Ranking process parameter association with low yield wafers using spec-out event network analysis", Computers & Industrial Engineering 113: 419-424.

2. Seokho Kang, Sungzoon Cho*, Su-jin Rhee, Kyung-Sang Yu (2017), "Reliable prediction of anti-diabetic drug failure using a reject option", Pattern Analysis and Applications 20(3): 883-891.

3. Misuk Kim, Seokho Kang, Jehyuk Lee, Hyunchang Cho, Sungzoon Cho*, Jee Su Park (2017), "Virtual metrology for copper-clad laminate manufacturing", Computers & Industrial Engineering 109: 280-287.

4. Seokho Kang, Pilsung Kang* (2017), "An intelligent virtual metrology system based on adaptive update for semiconductor manufacturing", Journal of Process Control 52: 66-74.

5. Seokho Kang, Eunji Kim, Jaewoong Shim, Sungzoon Cho*, Wonsang Chang, Junhwan Kim (2017), "Mining the relationship between production and customer service data for failure analysis of industrial products", Computers & Industrial Engineering 106: 137-146.

6. Seokho Kang, Dongil Kim*, Sungzoon Cho (2016), "Efficient feature selection based on random forward search for virtual metrology modeling", IEEE Transactions on Semiconductor Manufacturing 29(4): 391-398.

7. Jooseoung Park, Sungzoon Cho*, Seung-kyung Lee, Seokho Kang, Young Soo Kim, Ji Young Kim, Dong Seuk Choi (2015), "Energy-saving decision making framework for HVAC with usage logs", Energy and Buildings 108: 346-357.

8. Seokho Kang, Sungzoon Cho* (2015), "Optimal construction of one-against-one classifier based on meta-learning", Neurocomputing 167: 459-466.

9. Seokho Kang, Sungzoon Cho*, Daewoong An, Jaeyoung Rim (2015), "Using wafer map features to better predict die-level failures in final test", IEEE Transactions on Semiconductor Manufacturing 28(3): 431-437.

10. Seokho Kang, Sungzoon Cho, Pilsung Kang* (2015), "Multi-class classification via heterogeneous ensemble of one-class classifiers", Engineering Applications of Artificial Intelligence 43: 35-43.

11. Seokho Kang, Sungzoon Cho* (2015), "A novel multi-class classification algorithm based on one-class support vector machine", Intelligent Data Analysis 19(4): 713-725.

12. Seokho Kang, Pilsung Kang, Taehoon Ko, Sungzoon Cho*, Su-jin Rhee, Kyung-Sang Yu (2015), "An efficient and effective ensemble of support vector machines for anti-diabetic drug failure prediction", Expert Systems with Applications 42(9): 4265-4273.

13. Yongwon Park, Seokho Kang, Sungzoon Cho* (2015), "Memory die clustering and matching for optimal voltage window in semiconductor", IEEE Transactions on Semiconductor Manufacturing 28(2): 180-187.

14. Dongil Kim*, Pilsung Kang, Seung-kyung Lee, Seokho Kang, Seungyong Doh, Sungzoon Cho (2015), "Improvement of virtual metrology performance by removing metrology noises in a training dataset", Pattern Analysis and Applications 18(1): 173-189.

15. Seokho Kang, Sungzoon Cho, Pilsung Kang* (2015), "Constructing a multi-class classifier using one-against-one approach with different binary classifiers", Neurocomputing 149: 677-682.

16. Seokho Kang, Sungzoon Cho* (2014), "Approximating support vector machine with artificial neural network for fast prediction", Expert Systems with Applications 41(10): 4989–4995.

17. Seung-kyung Lee, Bongseok Kim, Minhoe Hur, Jooseoung Park, Seokho Kang, Sungzoon Cho*, Dongha Lee, Daehyung Lee (2014), "Knowledge discovery in inspection reports of marine structures", Expert Systems with Applications 41(4): 1153–1167.

18. Soojin Kim, Keunyoung Seo*, Seokho Kang, Sungzoon Cho (2013), "Organizational tenure diversity as predictors of combat performance in ROK army", Military Psychology 25(4): 345-353.