Classifying incomplete software engineering data using decision trees: An improved probabilistic approach

Proceedings of the 10th IASTED International Conference on Software Engineering and Applications, SEA 2006

Volumn , Issue , 2006, Pages 245-252

  Twala, Bhekisipho ^a   Cartwright, Michelle ^a   Liebchen, Gernot ^a  

^a BRUNEL UNIVERSITY   (United Kingdom)

Author keywords

Decision trees; Incomplete data; Software prediction

Indexed keywords

DECISION TREES; PROBABILISTIC LOGICS; SOFTWARE ENGINEERING; THEOREM PROVING;

INCOMPLETE DATA; MULTIDIMENSIONAL SOFTWARE HOUSE; PRIORI PROBABILITY; SOFTWARE PREDICTION;

DATA REDUCTION;

EID: 38049110017     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (22)

1. A. Agresti, Categorical Data Analysis, (New York: John Wiley, 1990).
2. L. Breiman, J. Friedman, R. Olshen, and C. Stone, Classification and Regression Trees, (Wadsworth, 1984).
3. B. Cestnik, I. Kononenko and I. Bratko, Assistant 86: a knowledge-elicitation tool for sophisticated users. In I. Bratko and N. Lavrac, editors, European Working Session on Learning - EWSL87. Sigma Press, Winslow, England, 1987.
4. th National Conference on Artificial Intelligence, 1996, 717-724, AAI, Press/MIT Press.
5. D.W. Hosmer and S. Lameshow, Applied Logistic Regression (New York: Wiley, 1989).
6. I. Kononenko, I. Bratko, and E. Roscar, Experiments in automatic learning of medical diagnostic rules. (Technical Report) Jozef Stefan Institute, Ljubljana, Yugoslavia, 1984.
7. R.J.A. Little and D.B. Rubin, Statistical Analysis with Missing Data, (New York: John Wiley, 1987).
8. O.O Lobo and M. Numao, Ordered Estimation of Missing Values. In Proc. of the 3rd Pacific-Asia Conference on Knowledge Discovery and Data Mining, Lecture Notes in Computer Science, 1574, 1999, 274-278.
9. O.O Lobo and M. Numao, On the Applicability of a Machine Learning Method for Estimating Missing Values. IMLC 2000, Palo Alto, California.
10. J.S. Long, Regression Models for Categorical and Limited Dependent Variables. Advanced Quantitative Techniques in the Social Sciences, Volume 7. (Sage Publications, 1997).
11. S. Press, Bayesian Statistics, (New York, Wiley, 1989).
12. J.R. Quinlan, Induction over Large Databases: Report HPP-79-14, Stanford University, 1979.
13. J.R. Quinlan, Learning efficient classification procedures and their application to chess and games. In R.S. Michalski, J.G Carbonelli, and T.M. Mitchell (Eds.), Machine Learning: An Artificial Intelligence approach. Los Altos: Morgan Kauffman, 1983.
14. J.R. Quinlan, Induction of decision trees, Machine Learning, 1, 1986, 81-106.
15. Quinlan, J.R. (1987). Simplifying decision trees, Internationaljournal of Man - Machine Studies, 27, 221-234.
16. J.R. Quinlan, C.4.5: Programs for machine learning (Los Altos, California: Morgan Kauffman Publishers, INC., 1993)
17. D.B. Rubin, Multiple Imputation for Nonresponse in Surveys (New York: John Wiley and Sons, 1987).
18. A. Shapiro, Structured Induction in Expert Systems (Addison Wesley, London, 1987).
19. T.M. Therneau and E.J. Atkinson, An Introduction to Recursive Partitioning Using the RPART Routines. Technical Report, Mayo Foundation, 1997.
20. Tukey, J.W. The Problem of Multiple Comparisons, Unpublished Manuscript, 1953.
21. B. Twala, Effective Techniques for Handling Incomplete Data Using Decision Trees, Unpublished PhD Thesis, Open University, UK, 2005.
22. A.P. White, Probabilistic Induction by dynamic path generation in virtual trees: In Brammer, Max (Eds.) Research & Development in Expert Systems: Cambridge: Cambridge University Press, 1987.