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Drawing conclusions from data?The rough set way

2000; Wiley; Volume: 16; Issue: 1 Linguagem: Inglês

10.1002/1098-111x(200101)16

1098-111X

Zdzis aw Pawlak,

Rough Sets and Fuzzy Logic

International Journal of Intelligent SystemsVolume 16, Issue 1 p. 3-11 Drawing conclusions from data—The rough set way Zdzisław Pawlak, Corresponding Author Zdzisław Pawlak [email protected] Institute of Theoretical and Applied Informatics, Polish Academy of Sciences, ul. Bałtycka 5, 44 000 Gliwice, PolandInstitute of Theoretical and Applied Informatics, Polish Academy of Sciences, ul. Bałtycka 5, 44 000 Gliwice, PolandSearch for more papers by this author Zdzisław Pawlak, Corresponding Author Zdzisław Pawlak [email protected] Institute of Theoretical and Applied Informatics, Polish Academy of Sciences, ul. Bałtycka 5, 44 000 Gliwice, PolandInstitute of Theoretical and Applied Informatics, Polish Academy of Sciences, ul. Bałtycka 5, 44 000 Gliwice, PolandSearch for more papers by this author First published: 01 December 2000 https://doi.org/10.1002/1098-111X(200101)16:1 3.0.CO;2-ICitations: 25AboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Abstract In the rough set theory with every decision rule two conditional probabilities, called certainty and coverage factors, are associated. These two factors are closely related with the lower and the upper approximation of a set, basic notions of rough set theory. It is shown that these two factors satisfy the Bayes' theorem. The Bayes' theorem in our case simply shows some relationship in the data, without referring to prior and posterior probabilities intrinsically associated with Bayesian inference in our case and can be used to "inverse" decision rules, i.e., to find reasons (explanation) for decisions. © 2001 John Wiley & Sons, Inc. References 1 Pawlak Z. Decision rules, Bayes' rule and rough sets. In: Zhong N, Skoron A, Ohsuga S, editors. New directions in rough sets, data mining, and granular—soft computing. Proceedings 7th International Workshop, RSFDGSC'99, Yamaguchi, Japan, November 1999. p 1– 9. 2 Łukasiewicz J. Die logishen Grundlagen der Wahrscheinilchkeitsrechnung. Krakow (1913). In: L Borkowski, editor. Jan Łukasiewicz—Selected works. Amsterdam, London: North Holland Publishing; Warsaw: Polish Scientific Publishers; 1970. 3Tsumoto S, Kobayashi S, Yokomori T, Tanaka H, Nakamura A, editors. Proceedings of the fourth international workshop on rough sets, fuzzy sets, and machine discovery (RSFD'96). The University of Tokyo, November 6–8, 1996. 4 Tsumoto S. Modelling medical diagnostic rules based on rough sets. In: Polkowski L, Skowron A, editors. Rough sets and current trends in computing. Lecture notes in artificial intelligence. Proceedings, First International Conference, RSCTC'98, Warsaw, Poland, June, 1998. p 475– 482. 5 Adams, EW. The logic of conditionals, an application of probability to deductive logic. Boston, Dordrecht: D Reidel Publishing Company; 1975. 6 Grzymała–Busse J. Managing uncertainty in expert systems. Boston, Dordrecht: Kluwer Academic Publishers; 1991. 7 Pawlak Z. Rough sets—theoretical aspects of reasoning about data. Boston, Dordrecht: Kluwer Academic Publishers; 1991. 8 Pawlak Z. Reasoning about data—a rough set perspective. In: Polkowski L, Skowron, A, editors. Rough sets and current trends in computing. Lecture notes in artificial intelligence. First international conference, RSCTC'98, Warsaw, Poland, June 1998. p 25– 34. 9 Pawlak Z, Skowron A. Rough membership functions. In: RR Yaeger, M Fedrizzi, J Kacprzyk, editors. Advances in the Dempster Shafer theory of evidence. New York: John Wiley & Sons, Inc.; 1994. p 251– 271. 10Polkowski L, Skowron A, editors. Rough sets and current trends in computing. Lecture notes in artificial intelligence. Proceedings First International Conference, RSCTC'98, Warsaw, Poland, June, 1998. 11 L Polkowski, A Skowron, editors. Rough sets in knowledge discovery. Physica-Verlag, Vol. 1, No. 2, 1998. 12 Skowron A. Management of uncertainty in AI: a rough set approach. In: Proceedings of the Conference SOFTEKS. Springer Verlag and British Computer Society; 1994. p 69– 86. 13 Ziarko W. Approximation region-based decision tables. In: L Polkowski, A Skowron, editors. Rough sets in knowledge discovery. Physica-Verlag, Vol. 1, No. 2, 1998. p 178– 185. Citing Literature Volume16, Issue1Special Issue: A Rough Set Approach to Reasoning About DataJanuary 2001Pages 3-11 ReferencesRelatedInformation