Improving the reliability of causal discovery from small data sets using argumentation

Journal of Machine Learning Research

Volumn 10, Issue , 2009, Pages 301-340

  Bromberg, Facundo ^a   Margaritis, Dimitris ^a  

^a Iowa State University   (United States)

Author keywords

Argumentation; Causal bayesian networks; Independence based causal discovery; Reliability improvement; Structure learning

Indexed keywords

ARGUMENTATION; CAUSAL BAYESIAN NETWORKS; INDEPENDENCE-BASED CAUSAL DISCOVERY; RELIABILITY IMPROVEMENT; STRUCTURE LEARNING;

DECODING; DISTRIBUTED PARAMETER NETWORKS; ERRORS; INFERENCE ENGINES; INTELLIGENT NETWORKS; KNOWLEDGE BASED SYSTEMS; RELIABILITY; SPEECH ANALYSIS; STATISTICAL TESTS;

BAYESIAN NETWORKS;

EID: 61749085851     PISSN: 15324435     EISSN: 15337928     Source Type: Journal    
DOI: None     Document Type: Article

References (29)

1. H. Abdi. The Bonferonni and Šidák corrections for multiple comparisons. In Neil Salkind, editor, Encyclopedia of Measurement and Statistics. Thousand Oaks (CA): Sage, 2007.
2. A. Agresti. Categorical Data Analysis. Wiley, 2nd edition, 2002.
3. L. Amgoud and C. Cayrol. A reasoning model based on the production of acceptable arguments. Annals of Mathematics and Artificial Intelligence, 34:197-215, 2002.
4. Y. Benjamini and Y. Hochberg. Controlling the false discovery rate: a practical and powerful approach to multiple testing. Journal of the Royal Statistical Society, Series B (Methodological), 57 ():289-300, 1995.
5. Y. Benjamini and D. Yekutieli. The control of the false discovery rate in multiple testing under dependency. Annals of Statistics, 29(4): 1165-1188, 2001.
6. 2 tests. Biometrics, 10:417-451, 1954.
7. T. H. Cormen, C. E. Leiserson, R. L. Rivest, and C. Stein. Introduction to Algorithms. MIT Press, 2nd edition, 2001.
8. C. L. Blake D. J. Newman, S. Hettich and C. J. Merz. UCI repository of machine learning databases. Irvine, CA: University of California, Department of Information and Computer Science, 1998.
9. A. R Dawid. Conditional independence in statistical theory. Journal of the Royal Statistical Society, 41:1-31, 1979.
10. R M. Dung. On the acceptability of arguments and its fundamental role in nonmonotonic reasoning, logic programming and n-person games. Artificial Intelligence, 77:321-357, 1995.
11. R Gärdenfors. Belief Revision. Cambridge Computer Tracts. Cambridge University Press, Cambridge, 1992.
12. R Gärdenfors and H. Rott. Belief revision. In Gabbay, D. M., Hogger, C. J. and Robinson, J. A., editors, Handbook of Logic in Artificial Intelligence and Logic Programming, volume 4. Clarendon Press, Oxford, 1995.
13. S. Hettich and S. D. Bay. The UCI KDD archive. Irvine, CA: University of California, Department of Information and Computer Science, 1999.
14. Y. Hochberg. A sharper Bonferroni procedure for multiple tests of significance. Biometrika, 75(4): 800-802, December 1988.
15. J. S. Ide, F. G. Cozman, and F. T. Ramos. Generating random Bayesian networks with constraints on induced width. Brazilian Symposium on Artificial Intelligence, Recife, Pernambuco, Brazil, 2002.
16. A. C. Kakas and F. Toni. Computing argumentation in logic programming. Journal of Logic and Computation, 9(4):515-562, 1999.
17. M. J. Kearns and U. V. Vazirani. An Introduction to Computational Learning Theory. MIT Press, Cambridge, MA, 1994.
18. R. R Loui. Defeat among arguments: a system of defeasible inference. Computational Intelligence, 2:100-106, 1987.
19. J. R Martins. Belief revision. In Shapiro, S. C., editor, Encyclopedia of Artificial Intelligence, pages 110-116. John Wiley & Sons, New York, second edition, 1992.
20. J. Pearl. Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. Morgan Kaufmann, San Francisco, CA, 2nd edition, 1988.
21. J. Pearl and A. Paz. GRAPHOIDS: A graph-based logic for reasoning about relevance relations. Technical Report 850038 (R-53-L), Cognitive Systems Laboratory, University of California, 1985.
22. J. L. Pollock. How to reason defeasibly. Artificial Intelligence, 57:1-42, 1992.
23. H. Prakken. Logical Tools for Modelling Legal Argument. A Study of Defeasible Reasoning in Law. KIuwer Law and Philosophy Library, Dordrecht, 1997.
24. H. Prakken and G. Vreeswijk. Logics for Defeasible Argumentation, volume 4 of Handbook of Philosophical Logic. Kluwer Academic Publishers, Dordrecht, 2 edition, 2002.
25. S. C. Shapiro. Belief revision and truth maintenance systems: An overview and a proposal. Technical Report CSE 98-10, Dept of Computer Science and Engineering, State University of New York at Buffalo, 1998.
26. R Spirtes, C. Glymour, and R. Scheines. Causation, Prediction, and Search. Adaptive Computation and Machine Learning Series. MIT Press, 2nd edition, January 2000.
27. J. D. Storey. A direct approach to false discovery rates. Journal of the Royal Statistical Society, Series B (Methodological), 64(3):479-498, 2002.
28. M. Studený. Conditional independence relations have no finite complete characterization. In Transactions of the 11th Prague Conference on Information Theory, Statistical Decision Functions and Random Processes, volume B, pages 377-396, 1991.
29. F. Toni and A. C. Kakas. In A. Nerodě, editor, 3rd International Conference on Logic Programming and Non-monotonic Reasoning, volume 928 of Lecture Notes in Artificial Intelligence, pages 401-415. Springer Verlag, 1995.