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Applying Rough Sets to Information Tables Containing Probabilistic Values

Michinori NakataContact Information and Hiroshi SakaiContact Information

(1)  Faculty of Management and Information Science, Josai International University, 1 Gumyo, Togane, Chiba, 283-8555, Japan
(2)  Department of Mathematics and Computer Aided Sciences, Faculty of Engineering, Kyushu Institute of Technology, Tobata, Kitakyushu, 804-8550, Japan
Abstract
Rough sets are applied to information tables containing imprecise values that are expressed in a probability distribution. A family of weighted equivalence classes is obtained where each equivalence class is accompanied by the probability to which it is an actual one. By using the family of weighted equivalence classes, we derive lower and upper approximations. The lower and upper approximations coincide with ones obtained from methods of possible worlds. Therefore, the method of weighted equivalence classes is justified. In addition, this method is applied to missing values interpreted probabilistically. Using weighted equivalence classes correctly derives a lower approximation, even in the case where the method of Kryszkiewicz does not derive any lower approximation.

Keywords  Rough sets - Imprecise information - Probabilistic value - Weighted equivalence class - Lower and upper approximations


Contact Information Michinori Nakata
Email: nakatam@ieee.org

Contact Information Hiroshi Sakai
Email: sakai@mns.kyutech.ac.jp
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