Biography

Prof. Chang-Hwan Lee

DongGuk University, South Korea


E-mail: chlee@dgu.ac.kr


Qualifications

1994  Ph.D., University of Connecticut

1988  M.S., Seoul National University

1982  B.A., Seoul National University


Publications (Selected)

  1. Chang-Hwan Lee “A Gradient Approach for Value Weighted Classification Learning in Naive Bayes” Knowledge-Based Systems, in press, 2015 .
  2. Chang-Hwan Lee and Soohwan Song “Improving Multi-label Classification of Documents Using Fine-Grained Weights” IEA/AIE 2015, Seoul, Korea, 2015
  3. Chang-Hwan Lee, “A Multi-Phase Approach for Classifying Multi-dimensional Sequence Data” Intelligent Data Analysis, Vol. 19, No. 3, 2015.
  4. Chang-Hwan Lee "An Information-Theoretic Filter Method for Feature Weighting in Naive Bayes“ International Journal of Pattern Recognition and Artificial Intelligence, Vol. 28, No. 5, 2014.
  5. Chang-Hwan Lee "A Hellinger-Based Importance Measure of Association Rules for Classification Learning" International Journal of Intelligent Systems, Volume 29, Issue 9, 2014.
  6. Chang-Hwan Lee, Fernando Gutierrez and Dejing Dou "Calculating Feature Weights in Naive Bayes with Kullback-Leibler Measure" International Conference on Data Mining, Vancouver, Dec. 11-14, 2011.
  7. Chang-Hwan Lee "Combining Different Classifiers with Identical Features in Co-Training Method" 7th International Conference on Machine Learning and Data Mining, Aug. 30-Sep. 3, New York, NY, 2011.
  8. Chang-Hwan Lee “Effects of Defection on Benefits of Artificial Group: A Game Theory Perspective " Journal of Korean Information Science Society, Vol. 38, No. 9, 2011.
  9. Chang-Hwan Lee " Calculating the Importance of Attributes in Naive Bayesian Classification Learning " Journal of Korean Electronic Engineering Society, Vol. 48, No. 5, 2011.
  10. Chang-Hwan Lee, Jungjin Yang, “A New Co-Training Method for Data Mining” in Data Mining and Management, (Book Chapter), Editors: Lawrence I. Spendler, Nova Science Publishers, 2010.
  11. Chang-Hwan Lee, “Information-Theoretic Method for Calculating Value Weights in Naïve Bayesian Learning” Journal of Korean Information Science Society, 37(6), 2010. 12.
  12. Chang-Hwan Lee, “Gradient Descent Approach for Value-Based Weighting” Journal of Korean Information Processing Society, 17B, 5, 2010. 10.
  13. Chang-Hwan Lee, Inchul-Jung, Young-Sik Kwon, “Committee Learning Classifier based Attribute Value Frequency” Journal of Korean Information Science Society, 37(4), 2010. 8.
  14. JongSik Yoon, Young S. Kwon and Chang-Hwan Lee, "Bankrupcy Prediction for Smale Businesses Using Credit Card Sales Information: Performance Comparison of Classification Algorithms" The 9th Asia Pacific Industrial Engineering & Management Systems Conference, Nusa Dua, Bali-Indonesia, December 3-5, 2008.
  15. Chang-Hwan Lee "IMSP: An Information Theoretic Approach for Multi-dimensional Sequential Pattern Mining" Volume 26, No. 3, pp. 231-242, Applied Intelligence, 2007. 6.
  16. Chang-Hwan Lee "A Hellinger-Based Discretization Method for Numeric Attributes in Classification Learning“ Knowledge-Based Systems, Vol. 20, No. 4, pp. 419-425, 2007. 5.
  17. Chang-Hwan Lee "Improving Classification Performance Using Unlabeled Data: Naive Bayesian Case", Knowledge-Based Systems, Vol. 20, No. 3, pp. 220-224, 2007. 4.
  18. Chang-Hwan Lee, "A Semi-Naive Bayesian Learning Method for Utilizing Unlabeled Data" International Conference on Knowledge-Based & Intelligent Information & Engineering Systems(KES), 2006. 10
  19. Chang-Hwan Lee, " Improving the Classification Accuracy Using Unlabeled Data: A Naive Bayesian Case" Journal of Information Processing Societ, 13(4), 2006. 8.
  20. Chang-Hwan Lee, "Inducing Sequential Patterns from Multidimensional Time Series Data", Australian AI Conference-2005, 2005. 12
  21. Chang-Hwan Lee, "Discretizing Continuous Attributes Using Information Theory", International Symposium on Computer and Information Sciences(ISCIS), 2005. 10.
  22. Chang-Hwan Lee, "An Entropy-based Approach for Generating Multi-dimensional Sequential Patterns" European Conference on Principles and Practice of Knowledge Discovery in Databases(PKDD), 2005. 10.
  23. Chang-Hwan Lee, " Calculating Attribute Weights in K-Nearest Neighbor Algorithms Using Information Theory" Journal of Korean Information Science Society, 2005. 9.
  24. Chang-Hwan Lee, “An Efficient Mining Algorithm for Generating Probabilistic Multidimensional Sequential Patterns” Journal of Korean Information Science Society, 2005. 2.
  25. Chang-Hwan Lee, Somin Lee, “An Improved Co-Training Method without Feature Split” Journal of Korean Information Science Society, 2004. 10.
  26. Chang-Hwan Lee, “Learning Multi-dimensional Sequential Patterns Using Hellinger Entropy Function” Journal of Korean Information Processing Society, 2004. 8.
  27. Chang-Hwan Lee, “An Integrated Method for Generating Inductive Rule Sets” Journal of Korean Information Processing Society, 2003. 2.
  28. Chang-Hwan Lee "Learning Inductive Rules Using Hellinger Measure" Applied Artificial Intelligence, Vol. 13, No. 8, 1999.
  29. Chang-Hwan Lee and Dong-Guk Shin "Using Hellinger Distance in a Nearest Neighbour   Classifier for Relational Databases", Knowledge-Based Systems, Vol. 12, No. 7, 1999.
  30. Chang-Hwan Lee and Dong-Guk Shin, "A Multistrategy Approach to Classification Learning in Databases” Data and Knowledge Engineering, Vol. 31, No. 1, pp. 67-93, 1999.
  31. Chang-Hwan Lee "Resolving Conflicts in Inheritance Reasoning with Statistical Approach" Chapter 3 in Artificial Intelligence and Automation, Vol. 3, (Book chapter), Editor N. G. Bourbakis, World Scientific Publisher, 1998.


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