Rough membership and bayesian confirmation measures for parameterized rough sets

Proceedings of SPIE - The International Society for Optical Engineering

Volumn 6104, Issue , 2006, Pages 314-324

  Greco, Salvatore ^a   Matarazzo, Benedetto ^a   Słowiński, Roman ^b  

^a UNIVERSITY OF CATANIA   (Italy)

^b POZNAN UNIVERSITY OF TECHNOLOGY   (Poland)

Author keywords

[No Author keywords available]

Indexed keywords

INFORMATION ANALYSIS; MATHEMATICAL MODELS; PARAMETER ESTIMATION;

BAYESIAN CONFIRMATION MEASURES; DATA RELATIONSHIPS; FREQUENCY DISTRIBUTION; PARAMETERIZED ROUGH SETS; ROUGH MEMBERSHIP;

ROUGH SET THEORY;

EID: 33645965931     PISSN: 0277786X     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (15)

1. Eells, E., Fitelson, B. (2002), Symmetries and assymetries in evidential support. Philosophical Studies, 107, 129-142
2. Fitelson, B. (2001), Studies in Bayesian Confirmation Theory. Ph.D. thesis, University of Wisconsin-Madison
3. Greco, S., Pawlak, Z., Słowiński, R. (2004), Can Bayesian confirmation measures be useful for rough set decision rules? Engineering Applications of Artificial Intelligence, 17, 345-361
4. Hilderman, R.J., Hamilton, H.J. (2001), Knowledge Discovery and Measures of Interest, Kluwer Academic Publishers, Boston
5. Pawlak, Z. (1982), Rough Sets. International Journal of Computer and Information Sciences, 11, 341-356
6. Pawlak, Z. (1991), Rough Sets, Kluwer, Dordrecht
7. Pawlak, Z. (2002), Rough Sets, decision algorithms and Bayes' Theorem. European Journal of Operational Research, 136, 181-189
8. Pawlak, Z., Skowron, A. (1994), Rough membership functions. [In]: R. R. Yager, M. Fedrizzi and J. Kacprzyk (eds.), Advances in the Dempster-Shafer Theory of Evidence, Wiley, New York, pp. 251-271
9. Popper, K. R. (1959), The Logic of Scientific Discovery, Hutchinson, London
10. Ślȩzak, D. (2005), Rough sets and Bayes factor. Transactions on Rough Sets III, LNCS 3400, 202-229
11. Ślȩzak, D. Ziarko, W. (2002), Bayesian rough set model. [In]: Proc. of Intl. Conference on Data Mining. Foundations of Data Mining and Knowledge Discovery Workshop, Meabashi City, Japan, pp. 131-136
12. Yao, Y.Y., 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. Lecture Notes in Artificial Intelligence, 1574, Springer-Verlag, Berlin, pp. 479-488
13. Ziarko, W. (1993), Variable precision Rough Sets Model, Journal of Computer and System Science, 46, 1, 39-59
14. Ziarko, W. (1994), Variable precision rough sets with asymmetric bounds. [In]: W. Ziarko (ed.), Rough sets, Fuzzy sets and Knowledge Discovery, Springer-Verlag, Berlin, pp. 167-177
15. Ziarko, W. (2001), Set approximation quality measures in the variable precision rough set model. [In]: Soft Computing Systems, Management and Applications, IOS Press, pp. 442-452