Bayesian confirmation measures within rough set approach

Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science)

Volumn 3066, Issue , 2004, Pages 264-273

  Greco, Salvatore ^a   Pawlak, Zdzisław ^b,c   Słowiński, Roman ^d,e  

^a UNIVERSITY OF CATANIA   (Italy)

^b INSTITUTE OF THEORETICAL AND APPLIED INFORMATICS   (Poland)

^c WARSAW SCHOOL OF INFORMATION TECHNOLOGY   (Poland)

^d POZNAN UNIVERSITY OF TECHNOLOGY   (Poland)

^e SYSTEMS RESEARCH INSTITUTE   (Poland)

Author keywords

[No Author keywords available]

Indexed keywords

DECISION THEORY; INFORMATION ANALYSIS; PATTERN RECOGNITION; ROUGH SET THEORY; THEOREM PROVING; COMPUTATION THEORY;

DECISION ATTRIBUTES; INDUCTIVE REASONING; MONOTONICITY; BAYESIAN CONFIRMATION; BAYESIAN CONFIRMATION MEASURES;

DATA REDUCTION; ROUGH SET THEORY;

EID: 9444299086     PISSN: 03029743     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1007/978-3-540-25929-9_31     Document Type: Conference Paper

References (38)

1. nd ed.) University of Chicago Press.
2. Christensen, D. (1999), Measuring Confirmation. Journal of Philosophy XCVI, 437-61.
3. Earman, J. (1992), Bayes or Bust: A Critical Examination of Bayesian Confirmation Theory. MIT Press, Cambridge.
4. Eells, E. (1982), Rational Decision and Causality. Cambridge University Press.
5. Eells, E., Fitelson, B. (2000), Measuring confirmation and evidence. Journal of Philosophy 97, 663-672.
6. Fitelson, B. (2001), Studies in Bayesian Confirmation Theory. Ph.D. thesis, University of Wisconsin - Madison.
7. Flach, P.A., Lachiche, N. (2001), Confirmation-guided discovery of first-order rules with tertius. Machine Learning 42, 61-95.
8. Gillies, D. (1986), In defense of the Popper-Miller argument. Philosophy of Science 53, 110-113.
9. Good, I. (1984), The best explicatum for weight of evidence. Journal of Statistical Computation and Simulation 19, 294-299.
10. Greco, S., Pawlak, Z., Słowiński, R. (2002), Generalized decision algorithms, rough inference rules, and flow graphs. [In]: J.J.Alpigini, J.F.Peters, A.Skowron, N.Zhong (eds.), Rough Sets and Current Trends in Computing. LNAI 2475, Springer, Berlin, pp. 93-104.
11. Greco, S., Pawlak, Z., Slowiński, R. (2004), Can Bayesian confirmation measures be useful for rough set decision rules? Submitted.
12. Hajek, P., Havranek, T. (1978), Mechanising Hypothesis Formulation: Mathematical Foundation for a General Theory. Springer-Verlag, Berlin.
13. Heckerman, D. (1988), An axiomatic framework for belief updates. [In]: L.Kanal & J. Lemmer (eds.), Uncertainty in Artificial Intelligence (2). Elsevier, New York, pp. 11-22.
14. Hilderman, R.J., Hamilton, H.J. (2002), Knowledge Discovery and Measures of Interest, Kluwer, Boston.
15. Horwich, P.(1982), Probability and Evidence. Cambridge University Press.
16. Horvitz, E. and Heckerman, D. (1986), The inconsistent use of certainty measures in artificial intelligence research. In L. Kanal and J. Lemmer (eds.), Uncertainty in Artificial Intelligence (1). Elsevier, New York, pp. 137-151.
17. Jeffrey, R. (1992), Probability and the Art of Judgment. Cambridge University Press.
18. Joyce, J. (1999), The Foundations of Causal Decision Theory. Cambridge University Press.
19. Kyburg, H. (1983), Recent work in inductive logic. [In]: Lucey and Tabor (eds.), Recent Work in Philosophy. Rowman and Allanfield, Totowa, N.J., pp. 89-150.
20. Keynes, J.(1921), A Treatise on Probability. Macmillan, London.
21. Łukasiewicz, J. (1913), Die Logische Grundlagen der Wahrscheinlichkeitsrechnung, Cracow; English translation in: L. Borkowski (ed.), Jan Łukasiewicz - Selected Works, North Holland & Polish Scientific Publishers, Amsterdam-Warsaw, 1970.
22. Mackie, J. L. (1963), The paradox of confirmation. The British Journal for the Philosophy of Science 13, 265-277.
23. Mackie, J. (1969), The relevance criterion of confirmation. The British Journal for the Philosophy of Science 20, 27-40.
24. Milne, P. (1995), A Bayesian defence of Popperian science? Analysis 55, 213-215.
25. Milne, P. (1996), Log [P(h|e)/P(h|b)] is the one true measure of confirmation. Philosophy of Science 63, 21-26.
26. Pawlak, Z. (1982), Rough Sets. Int. J. of Computer and Information Sciences 11, 341-356.
27. Pawlak, Z. (1991), Rough Sets - Theoretical Aspects of Reasoning about Data. Kluwer Academic Publishers, Boston.
28. Pawlak, Z. (2002), Rough Sets, decision algorithms and Bayes' Theorem. European Journal of Operational Research 136, 181-189.
29. Pawlak, Z. (2003), Flow graphs and decision algorithms. [In]: G. Wang, Q. Liu, Y.Y. Yao, A. Skowron (eds.), Rough Sets, Fuzzy Sets, Data Mining and Granular Computing. LNAI 22639, Springer, Berlin, pp. 1-11.
30. Pawlak, Z. (2004), Decision networks. [In]: J.W. Grzymala-Busse, J. Komorowski, R. Słowiński, S. Tsumoto (eds.), Rough Sets and Current Trends in Computing. LNAI, Springer-Verlag, Berlin, in this volume.
31. Pearl, J. (1988), Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. Morgan Kaufman, San Francisco.
32. Pollard, S. (1999), Milne's measure of confirmation. Analysis 59, 335-337.
33. Schlesinger, G. (1995), Measuring degrees of confirmation. Analysis 55, 208-212.
34. Rosenkrantz, R. (1994), Bayesian confirmation: paradise regained. The British Journal for the Philosophy of Science 45, 461-476.
35. Schum, D. (1994), The Evidential Foundations of Probabilistic Reasoning. Wiley, New York.
36. Tsumoto, S. (2002), Statistical test for rough set approximation based on Fisher's exact test. [In]: J. J. Alpigini, J. F. Peters, A. Skowron, N. Zhong (eds.), Rough Sets and Current Trends in Computing. LNAI 2475, Springer, Berlin, 2002, pp. 381-388.
37. Yao, Y.Y., and Zhong, N. (1999), An analysis of quantitative measures associated with rules. [In]: N. Zhong and L. Zhou (eds.), Methodologies for Knowledge Discovery and Data Mining. LNAI 1574, Springer, Berlin, pp. 479-488.
38. Zembowicz, R., Zytkow, J. (1996), From contingency tables to various forms of knowledge in databases. [In]: U. M. Fayyad, G. Piatetsky-Shapiro, P. Smyth, R. Uthurusamy, (eds.), Advances in Knowledge Discovery and Data Mining, AAAI Press & The MIT Press, Menlo Park and Cambridge, pp. 329-349.