Predicting incomplete data on the basis of non symmetric similarity relation
نویسندگان
چکیده
The rough sets theory was meant as a tool for imprecise and inconsistent information systems. Incomplete information can be also considered as a particular case of imprecise information. Because the rough sets theory makes the assumption of completeness of all attributes of input vector, many modifications of this theory were developed describing how to use the incomplete data. This article presents basic approaches: tolerance relation and non symmetric similarity relation. Furthermore, a new method of supplementing some incomplete objects from an information table has been proposed.
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On the Extension of Rough Sets under Incomplete Information
The rough set theory, based on the indiscernibility relation, is not useful for analysing incomplete information. Therefore, we introduce two generalizations of this theory, besides the well known one based on tolerance relations. The rst proposal is based on non symmetric similarity relations, while the second one uses valued tolerance relation. Both approaches provide more informative results...
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