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Applying Rough Sets to Information Tables Containing Probabilistic Values
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Applying Rough Sets to Information Tables Containing Probabilistic Values
Michinori Nakata1 and Hiroshi Sakai2 
| (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
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