| EUS SVMs: Ensemble of under-sampled SVMs for data imbalance problems P Kang, S Cho International Conference on Neural Information Processing, 837-846, 2006 | 178 | 2006 |
| Continual retraining of keystroke dynamics based authenticator P Kang, S Hwang, S Cho International Conference on Biometrics, 1203-1211, 2007 | 95 | 2007 |
| A virtual metrology system for semiconductor manufacturing P Kang, H Lee, S Cho, D Kim, J Park, CK Park, S Doh Expert Systems with Applications 36 (10), 12554-12561, 2009 | 93 | 2009 |
| Virtual metrology for run-to-run control in semiconductor manufacturing P Kang, D Kim, H Lee, S Doh, S Cho Expert Systems with Applications 38 (3), 2508-2522, 2011 | 87 | 2011 |
| Keystroke dynamics-based user authentication using long and free text strings from various input devices P Kang, S Cho Information Sciences 308, 72-93, 2015 | 71 | 2015 |
| Locally linear reconstruction for instance-based learning P Kang, S Cho Pattern Recognition 41 (11), 3507-3518, 2008 | 65 | 2008 |
| Machine learning-based novelty detection for faulty wafer detection in semiconductor manufacturing D Kim, P Kang, S Cho, H Lee, S Doh Expert Systems with Applications 39 (4), 4075-4083, 2012 | 62 | 2012 |
| Box office forecasting using machine learning algorithms based on SNS data T Kim, J Hong, P Kang International Journal of Forecasting 31 (2), 364-390, 2015 | 56 | 2015 |
| Pre-launch new product demand forecasting using the Bass model: A statistical and machine learning-based approach H Lee, SG Kim, H Park, P Kang Technological Forecasting and Social Change 86, 49-64, 2014 | 49 | 2014 |
| Locally linear reconstruction based missing value imputation for supervised learning P Kang Neurocomputing 118, 65-78, 2013 | 45 | 2013 |
| An efficient and effective ensemble of support vector machines for anti-diabetic drug failure prediction S Kang, P Kang, T Ko, S Cho, S Rhee, KS Yu Expert Systems with Applications 42 (9), 4265-4273, 2015 | 42 | 2015 |
| Multi-co-training for document classification using various document representations: TF–IDF, LDA, and Doc2Vec D Kim, D Seo, S Cho, P Kang Information Sciences 477, 15-29, 2019 | 39 | 2019 |
| Constructing a multi-class classifier using one-against-one approach with different binary classifiers S Kang, S Cho, P Kang Neurocomputing 149, 677-682, 2015 | 39 | 2015 |
| Semi-supervised support vector regression based on self-training with label uncertainty: An application to virtual metrology in semiconductor manufacturing P Kang, D Kim, S Cho Expert Systems with Applications 51, 85-106, 2016 | 33 | 2016 |
| Multi-class classification via heterogeneous ensemble of one-class classifiers S Kang, S Cho, P Kang Engineering Applications of Artificial Intelligence 43, 35-43, 2015 | 33 | 2015 |
| Probabilistic local reconstruction for k-NN regression and its application to virtual metrology in semiconductor manufacturing S Lee, P Kang, S Cho Neurocomputing 131, 427-439, 2014 | 33 | 2014 |
| A hybrid novelty score and its use in keystroke dynamics-based user authentication P Kang, S Cho Pattern recognition 42 (11), 3115-3127, 2009 | 32 | 2009 |
| Improvement of keystroke data quality through artificial rhythms and cues P Kang, S Park, S Hwang, H Lee, S Cho computers & security 27 (1-2), 3-11, 2008 | 31 | 2008 |
| Sentiment classification with word localization based on weakly supervised learning with a convolutional neural network G Lee, J Jeong, S Seo, CY Kim, P Kang Knowledge-Based Systems 152, 70-82, 2018 | 30 | 2018 |
| Box-office forecasting based on sentiments of movie reviews and Independent subspace method M Hur, P Kang, S Cho Information Sciences 372, 608-624, 2016 | 30 | 2016 |
sungzoon choSeoul National UniversityVerified email at snu.ac.kr
