Incorporating an EM-approach for handling missing attribute-values in decision tree induction

Proceedings - HIS 2005: Fifth International Conference on Hybrid Intelligent Systems

Volumn 2005, Issue , 2005, Pages 309-314

  Karmaker, Amitava ^a   Kwek, Stephen ^a  

^a University of Texas at San Antonio   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

CLASSIFICATION (OF INFORMATION); ITERATIVE METHODS; LEARNING SYSTEMS; TREES (MATHEMATICS);

DECISION TREE INDUCTION; EXPECTATION MAXIMIZATION(EM);

DECISION THEORY;

EID: 33846976186     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICHIS.2005.64     Document Type: Conference Paper

References (20)

1. C. L. Blake, C. Mertz. UCI Repository of Machine Learning Databases, 1998.
2. A. Dardzinska, Z.W. Ras. CHASE-2: Rule based chase algorithm for information systems of type lambda. To appear in Proceedings of the Second International Workshop on Active Mining (AM'2003), Maebashi City, Japan, LNAI, Springer-Verlag, 2005.
3. A.P. Dempster, N. M. Laird, D. B. Rubin. Maximum-likelihood from Incomplete Data via the EM Algorithm. Journal of the Royal Statistical Society, B39: 1-38, 1977.
4. B. D. Gerardo, J. Lee, J. Lee, M. Park, M. Lee. The Association Rule Algorithm with Missing Data in Data Mining. In: International Conference on Computational Science and Its Applications. 97-105, 2004.
5. J. W. Grzymala-Busse. On the Unknown Attribute Values in Learning from Examples.. Proc. of the ISMIS-91m 6th International Symposium on Methodlogies for Intelligent Systems. Lecture Notes in Artificial Intelligent, vol 542. Springer-Verlag, 368-377, 1991.
6. J. W. Grzymala-Busse, M. Hu. A Comparison of Several Approaches to Missing Attribute Values in Data Mining. Rough Sets and Current Trends in Computing. 378-385, 2000.
7. E. A. Gustavo, P. A. Batista, M. C. Monard. An Analysis of Four Missing Data Treatment Methods for Supervised Learning. Applied Artificial Intelligence. 17(5-6), 519-533, 2003.
8. E. R. Hruschka Jr., N. F. F. Ebecken. Missing values prediction with K2. Intelligent Data Analysis. 6(6), 557-566, 2002.
9. S. Ikeda. Acceleration of the EM algorithm. Systems and Computers in Japan 31(2): 10-18, 2000.
10. C. Huang, H. Lee. A Grey-Based Nearest Neighbor Approach for Missing Attribute Value Prediction. Applied Artificial Intelligence. 20(3), 239-252, 2004.
11. R. J. A. Little, D. B. Rubin. Statistical Analysis with Missing Data. Wiley, New York, second edition, 2002.
12. G. J. McLachlan, T. Krishnan. The EM Algorithm and Extensions. Wiley, New York, 1997.
13. J. R. Nayak, D. J. Cook. Approximate Association Rule Mining. In: Proceedings of the Florida Artificial Intelligence Research Symposium, 2001.
14. L. Ortiz, L. Kaelbling. Accelerating EM: An Empirical Study. Proceedings of Uncertainty in Artificial Intelligence, 512-521,1999.
15. B. Petersen, O. Winther, L. K. Hansen. On the Slow Convergence of EM and VBEM in Low-Noise Linear Models Neural Computation, September 2005, vol. 17, no. 9, pp. 1921-1926(6)
16. J. R. Quinlan. Induction of decision trees. Machine Learning, 1(1): pp. 81-106,1986.
17. A. Ragel. Preprocessing of Missing Values Using Robust Association Rules. In: proceedings of The Second Pacific-Asia Conference, 1998.
18. A. Ragel, B. Cremilleux. MVC - A Preprocessing Method to deal with missing values. Knowledge based system, vol. 12, pp. 285-291, 1999.
19. I. H. Witten, E. Frank. Nuts and bolts: Machine learning algorithms in Java. In: Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations, pp. 265-320. Morgan-Kaufmann, 2000.
20. C. Wu, C. Wun, H. Chou. Using Association Rules for Completing Missing Data. In: Fourth International Conference on Hybrid Intelligent Systems (HIS), 236-241, 2004.