Missing values prediction with K2

Intelligent Data Analysis

Volumn 6, Issue 6, 2002, Pages 557-566

  Hruschka Jr , Estevam R ^a   Ebecken, Nelson F F ^a  

^a FEDERAL UNIVERSITY OF RIO DE JANEIRO   (Brazil)

Author keywords

classification and prediction; missing values

Indexed keywords

BAYESIAN NETWORKS; DATA MINING; FORECASTING;

BAYESIAN ALGORITHMS; BAYESIAN CONDITIONING; CLASSIFICATION TOOL; DATA MINING PROBLEMS; DATA PREPARATION; DATA SET; MISSING VALUES;

CLASSIFICATION (OF INFORMATION);

EID: 0242563303     PISSN: 1088467X     EISSN: 15714128     Source Type: Journal    
DOI: 10.3233/ida-2002-6606     Document Type: Article

References (45)

1. M. Ramoni, P. Sebastiani, An Introduction to Bayesian Robust Classifier, KMI Technical Report KMI-TR-79, Knowledge Media Institute, The Open University, 1999.
2. R. Kohavi, Scaling up the Accuracy of Naive-Bayes Classifiers: a Decision tree hybrid, in Proccedings of the Second International Conference on Knowledge Discovery and Data Mining, San Mateo CA, Morgan Kaufman, 1996.
3. N. Friedman, D. Geiger and M. Goldszmidt, Bayesian Network Classifiers, Machine Learning, 1997.
4. P. Domingos and M. Pazzani, On the Optimality of the simple bayesian classifier under zero-one loss, Machine Learning 29 (1997), 103-130.
5. G. Cooper and E. Herskovitz, A Bayesian Method for the Induction of Probabilistic Networks from Data, Machine Learning, 9 (1992), 309-347.
6. J. Pearl, Probabilistic Reasoning in Intelligent Systems, Morgan Kaufmann, 1988.
7. F.V. Jensen, An Introduction to Bayesian Networks. Springer-Verlag, New York, 1996.
8. E. Castillo, J.M. Gutíerrez, A.S. Hadi, Expert Systems and Probabilistic Network Models, Monographs in Computer Science, Springer-Verlag, 1997.
9. D.J. Spiegelhalter and S.L. Lauritzen, Sequential updating of conditional probability on direct graphical structures, Networks 20 (1990), 576-606.
10. M.H. DeGroot, Optimal statistical decisions, McGraw-Hill, New York, 1970.
11. J. Han and M. Kamber, Data Mining: Concepts and Techniques, Morgan Kaufmann, 2001.
12. W.Z. Liu, A.P. White, S.G. Thompson and M.A. Bramer, Techniques for Dealing with Missing Values in Classification, Advances in Intelligent Data Analysis, Lecture Notes in Computer Science, LNCS1280, 1997, pp. 527-536.
13. J. Quinlan, Induction of Decision Trees, Machine Learning 1 (1986), 81-106.
14. A.P. White, Probabilistic Induction by Dynamic Path Generation in Virtual Trees, in: Research and Development in Expert Systems III, M.A. Bramer, 1987, pp. 35-46, Cambridge, Cambridge University Press.
15. I. Kononenko, I. Bratko and E. Roskar, Experiments in Automatic Learning of Medical Diagnostic Rules, Technical Report, Jozef Stefan Institute, Ljubjana, Yogoslavia, 1984.
16. A. Shapiro, Private Communication, 1983.
17. J.R. Quinlan, Unknown Attribute Values in Induction. Proceedings of 6th International Workshop on Machine Learning, 164-168, Ithaca, NY, 1989.
18. J.R. Quinlan, C4.5 Program for Machine Learning, Morgan Kaufmann, 1993.
19. B. Cestnik, I. Kononenko and I. Bratko, Assistant-86: A Knowledge-Elicitation Tool for Sophisticated Users, in I. Bratko and N. Lavrac, eds, Progress in Machine Learning, Wilmslow, Sigma Press, 1987.
20. O.O. Lobo and M. Numao, On the Applicability of Machine Learning Method for Estimating Missing Values, WhatWork Well Where, Workshop as part of the 17th International Conference on Machine Learning, 2000.
21. O.O. Lobo and M. Numao, Ordered Estimation of Missing Values for Propositional Learning, Journal of the Japanese Society for Artificial Intelligence, vol. 15, n ? 1, January, 2000.
22. H.F. Friedman, R. Kohavi and Y. Yun, Lazy Decision Trees, in Proceedings of the 13th National Conference on Artificial Intelligence, AAAI Press/MIT Press, 1996, pp. 717-724.
23. R.E. Schapire, Y. Freund, P. Bartlett and W.S. Lee, Boosting the Margin: A New Explanation for the Effectiveness of Voting Methods, Proceedings of the 14th International Conference on Machine Learning, Morgan Kaufmann, 1997, pp. 352-330.
24. L. Breiman, Bagging Predictors, Machine Learning, 24 (1996), 123-140.
25. Z. Zheng and G.I.Webb, Stochastic Attribute Selection Committees, in Proceedings of the 10th Australian Joint Conference of Artificial Intelligence, Berlin: Springer-Verlag, 1998.
26. Z. Zheng and G.I. Webb, Stochastic Attribute Selection Committees with Multiple Boosting: Learning more Accurate and more Stable Classifier Committees, in Proceedings of the 3rd Pacific-Asia Conference on Knowledge Discovery and Data Mining, Berlin: Springer-Verlag, 1999.
27. Z. Zheng and B.T. Low, Classifying Unseen Cases with Many Missing Values. Proceedings of PAKDD'99, Berlin, Springer-Verlag, 1999.
28. P. Sebastiani and M. Ramoni, Bayesian Inference with Missing Data Using Bound and Collapse, Technical Report KMI-TR-58, KMI, Open University, 1997.
29. D.B. Rubin, Inference and Missing Data, Biometrika, 63 (1976), 581-592.
30. R. Little and D.B. Rubin, Statistical Analysis with Missing Data, Wiley, New York, 1987.
31. A. Gelman, J.B. Carlin, H.S. Stern and D.B. Rubin, Bayesian Data Analysis, Chapman and Hall, London, 1995.
32. W.R. Gilks, S. Richardson and D.J. Spiegelhalter, Markov Chain Monte Carlo in Practice, Chapman and Hall, London, 1996.
33. W.R. Gilks and G.O. Roberts, Strategies for Improving MCMC, inW.R. Gilks, S. Richardson and D.J. Spiegelhalter, eds, Markov Chain Monte Carlo in Practice, 1996, pp. 89-114, Chapman and Hall, London.
34. D.B. Rubin, Formalizing Subjective Notion about the Effects of Nonrespondents in Samples Surveys, Journal of the American Statistical Association 72 (1977), 538-543.
35. D.B. Rubin, Multiple Imputation for non Responses in Surveys, New York, John Wiley & Sons, 1987.
36. A.P. Dempster, N.M. Laird and D.B. Rubin, Maximum Likelihood from Incomplete Data via the EM algorithm, Journal of the Royal Statistical Society B 39 (1977), 1-39.
37. N. Friedman, Learning Belief Networks in the presence of Missing Values and Hidden Variables, Proceedings of the 14th International Conference on Machine Learning, 1997.
38. W. Lam and F. Bacchus, Learning Bayesian Belief Networks, Na Approach based on the MDL principle, Computational Intelligence, 10 (1994), 269-293.
39. G. Schwartz, Estimating the Dimension of a Model, Annals of Statistics, 6 (1978), 461-464.
40. D. Heckerman, A. Mamdani and M.P.Wellman, Real-World Applications of Bayesian Networks, Communications of the ACM, 38 (1995).
41. V. Didelez and I. Pigeot, Maximum Likelihood Estimation in Graphical Models with Missing Values, Biometrika, 85 (1998).
42. M. Frydenberg and S.L. auritzen, Decomposition of Maximal Likelihood in Mixed Graphicals Interactions Models, Biometrika, 76 (1989), 539-555.
43. S.L. Lauritzen, The EM Algorithm for Graphical Association Models with Missing Data, Computational Statistics and Data Analysis, 19 (1995), 191-201.
44. A.A. Knopf, Mushroom records drawn from The Audubon Society Field Guide to North American Mushrooms, G.H. Lincoff Pres., New York, 1981.
45. W.H. Press, S.A. Teukolsky, W.T. Vetterling and B.P. Flannery, Numerical Recipes in C: The Art of Scientific Computing, Second Edition, Cambridge University Press, 1992.