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Chang-Hwan Lee 0001
Person information
- affiliation: Dongguk University, Department of Information and Communications, Seoul, Korea
Other persons with the same name
- Chang-Hwan Lee 0002 — Pohang University of Science and Technology (POSTECH), South Korea
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2020 – today
- 2023
- [j10]Chanseung Lee, Chang-Hwan Lee:
The Effect of Defection in Maximizing Group Benefit. Appl. Artif. Intell. 37(1) (2023)
2010 – 2019
- 2018
- [j9]Chang-Hwan Lee:
An information-theoretic filter approach for value weighted classification learning in naive Bayes. Data Knowl. Eng. 113: 116-128 (2018) - [j8]Chang-Hwan Lee:
Multi-label classification of documents using fine-grained weights and modified co-training. Intell. Data Anal. 22(1): 103-115 (2018) - 2016
- [c12]Chang-Hwan Lee:
Calculating different weights in feature values in logistic regression. ICCIP 2016: 148-150 - [c11]Chang-Hwan Lee:
Assigning Different Weights to Feature Values in Naive Bayes. SCDS 2016: 171-179 - 2015
- [j7]Chang-Hwan Lee:
A multi-phase approach for classifying multi-dimensional sequence data. Intell. Data Anal. 19(3): 547-561 (2015) - [j6]Chang-Hwan Lee:
A gradient approach for value weighted classification learning in naive Bayes. Knowl. Based Syst. 85: 71-79 (2015) - [c10]Soohwan Song
, Chang-Hwan Lee:
Improving Document Classification Using Fine-Grained Weights. IEA/AIE 2015: 488-492 - [e1]Moonis Ali, Young Sig Kwon, Chang-Hwan Lee, Juntae Kim, Yongdai Kim:
Current Approaches in Applied Artificial Intelligence - 28th International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2015, Seoul, South Korea, June 10-12, 2015, Proceedings. Lecture Notes in Computer Science 9101, Springer 2015, ISBN 978-3-319-19065-5 [contents] - 2014
- [j5]Chang-Hwan Lee:
A Hellinger-Based Importance Measure of Association Rules for Classification Learning. Int. J. Intell. Syst. 29(9): 807-822 (2014) - [j4]Chang-Hwan Lee:
An Information-Theoretic filter Method for Feature Weighting in Naive Bayes. Int. J. Pattern Recognit. Artif. Intell. 28(5) (2014) - [c9]Chang-Hwan Lee:
A New Fine-Grained Weighting Method in Multi-Label Text Classification. MAICS 2014: 26-30 - 2013
- [c8]Chang-Hwan Lee, Young-Tak Kim:
QoS-aware hierarchical token bucket (QHTB) queuing disciplines for QoS-guaranteed Diffserv provisioning with optimized bandwidth utilization and priority-based preemption. ICOIN 2013: 351-358 - 2012
- [c7]Kwangtae Jeong, Chang-Hwan Lee, Young-Tak Kim:
Performance analysis of multi-layered protection switching with control-forwarding separation. APNOMS 2012: 1-4 - 2011
- [c6]Chang-Hwan Lee, Fernando Gutierrez, Dejing Dou:
Calculating Feature Weights in Naive Bayes with Kullback-Leibler Measure. ICDM 2011: 1146-1151 - [c5]Chang-Hwan Lee:
Combining Different Classifiers with Identical Features in Co-Training Method. MLDM Posters 2011: 49-58
2000 – 2009
- 2007
- [j3]Chang-Hwan Lee:
IMSP: An information theoretic approach for multi-dimensional sequential pattern mining. Appl. Intell. 26(3): 231-242 (2007) - [j2]Chang-Hwan Lee:
Improving classification performance using unlabeled data: Naive Bayesian case. Knowl. Based Syst. 20(3): 220-224 (2007) - [j1]Chang-Hwan Lee:
A Hellinger-based discretization method for numeric attributes in classification learning. Knowl. Based Syst. 20(4): 419-425 (2007) - 2006
- [c4]Chang-Hwan Lee:
A Semi-naive Bayesian Learning Method for Utilizing Unlabeled Data. KES (1) 2006: 187-194 - 2005
- [c3]Chang-Hwan Lee:
Inducing Sequential Patterns from Multidimensional Time Series Data. Australian Conference on Artificial Intelligence 2005: 900-903 - [c2]Chang-Hwan Lee:
Discretizing Continuous Attributes Using Information Theory. ISCIS 2005: 493-502 - [c1]Chang-Hwan Lee:
An Entropy-Based Approach for Generating Multi-dimensional Sequential Patterns. PKDD 2005: 585-592
Coauthor Index
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