Rough classification in incomplete information systems

Rough Set Approach to Incomplete Information Systems

In the paper we present Rough Set approach to reasoning in incomplete information systems. We propose reduction of knowledge that eliminates only that information, which is not essential from the point of view of classification or decision making. In our approach we make only one assumption about unknown values: the real value of a missing attribute is one from the attribute domain. However, we...

Multigranulation decision-theoretic rough sets in incomplete information systems

We study multigranulation decision-theoretic rough sets in incomplete information systems. Based on Bayesian decision procedure, we propose the notions of weighted mean multigranulation decision-theoretic rough sets, optimistic multigranulation decision-theoretic rough sets, and pessimistic multigranulation decision-theoretic rough sets in an incomplete information system. We investigate the re...

Extension of Rough Set under Incomplete Information Systems

A Modified Rough Set Approach to Incomplete Information Systems

The key point of the tolerance relation or similarity relation presented in the literature is to assign a “null” value to all missing attribute values. In other words, a “null” value may be equal to any value in the domain of the attribute values. This may cause a serious effect in data analysis and decision analysis because the missing values are just “missed” but they do exist and have an inf...

Probabilistic Rough Classification in Information Systems with Fuzzy Decision Attributes

Based on Pawlak’s two way approximations on Rough Sets and using thresholds G.Ganesan et al in 2004 proposed a method of rough indexing an information system which has fuzzy decision attributes. The limitation of Pawlak’s approximation is that it does not quantify the level of importance of the basic granules. Recently, Y.Y.Yao discussed Probabilistic Rough Set Model, which specified how basic ...