Chang-Hwan Lee

23 papers A* 1C 3Journal 10Unranked 9

Year	Rank	Type	Title / Venue / Authors
2023	J	`jnl`	The Effect of Defection in Maximizing Group Benefit. Appl. Artif. Intell. Chanseung Lee, Chang-Hwan Lee
2018	J	`jnl`	An information-theoretic filter approach for value weighted classification learning in naive Bayes. Data Knowl. Eng. Chang-Hwan Lee
2018	J	`jnl`	Multi-label classification of documents using fine-grained weights and modified co-training. Intell. Data Anal. Chang-Hwan Lee
2016	—	`conf`	Assigning Different Weights to Feature Values in Naive Bayes. SCDS Chang-Hwan Lee
2016	—	`conf`	Calculating different weights in feature values in logistic regression. ICCIP Chang-Hwan Lee
2015	J	`jnl`	A gradient approach for value weighted classification learning in naive Bayes. Knowl. Based Syst. Chang-Hwan Lee
2015	J	`jnl`	A multi-phase approach for classifying multi-dimensional sequence data. Intell. Data Anal. Chang-Hwan Lee
2015	C	`ed.`	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 IEA/AIE Moonis Ali, Young Sig Kwon, Chang-Hwan Lee, Juntae Kim, Yongdai Kim
2015	C	`conf`	Improving Document Classification Using Fine-Grained Weights. IEA/AIE Soohwan Song, Chang-Hwan Lee
2014	J	`jnl`	A Hellinger-Based Importance Measure of Association Rules for Classification Learning. Int. J. Intell. Syst. Chang-Hwan Lee
2014	—	`conf`	A New Fine-Grained Weighting Method in Multi-Label Text Classification. MAICS Chang-Hwan Lee
2014	J	`jnl`	An Information-Theoretic filter Method for Feature Weighting in Naive Bayes. Int. J. Pattern Recognit. Artif. Intell. Chang-Hwan Lee
2013	—	`conf`	QoS-aware hierarchical token bucket (QHTB) queuing disciplines for QoS-guaranteed Diffserv provisioning with optimized bandwidth utilization and priority-based preemption. ICOIN Chang-Hwan Lee, Young-Tak Kim
2012	C	`conf`	Performance analysis of multi-layered protection switching with control-forwarding separation. APNOMS Kwangtae Jeong, Chang-Hwan Lee, Young-Tak Kim
2011	A*	`conf`	Calculating Feature Weights in Naive Bayes with Kullback-Leibler Measure. ICDM Chang-Hwan Lee, Fernando Gutierrez, Dejing Dou
2011	—	`conf`	Combining Different Classifiers with Identical Features in Co-Training Method. MLDM Posters Chang-Hwan Lee
2007	J	`jnl`	A Hellinger-based discretization method for numeric attributes in classification learning. Knowl. Based Syst. Chang-Hwan Lee
2007	J	`jnl`	IMSP: An information theoretic approach for multi-dimensional sequential pattern mining. Appl. Intell. Chang-Hwan Lee
2007	J	`jnl`	Improving classification performance using unlabeled data: Naive Bayesian case. Knowl. Based Syst. Chang-Hwan Lee
2006	—	`conf`	A Semi-naive Bayesian Learning Method for Utilizing Unlabeled Data. KES (1) Chang-Hwan Lee
2005	—	`conf`	An Entropy-Based Approach for Generating Multi-dimensional Sequential Patterns. PKDD Chang-Hwan Lee
2005	—	`conf`	Discretizing Continuous Attributes Using Information Theory. ISCIS Chang-Hwan Lee
2005	—	`conf`	Inducing Sequential Patterns from Multidimensional Time Series Data. Australian Conference on Artificial Intelligence Chang-Hwan Lee