Incomplete Information Tables and Rough Classification

Rough Sets, Coverings and Incomplete Information

Rough sets are often induced by descriptions of objects based on the precise observations of an insufficient number of attributes. In this paper, we study generalizations of rough sets to incomplete information systems, involving imprecise observations of attributes. The precise role of covering-based approximations of sets that extend the standard rough sets in the presence of incomplete infor...

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...

Rough sets and decision tables

Rough Set Approach to Information Tables with Imprecise Decisions

In this paper, we treat information tables with imprecise decisions, for short, imprecise decision tables. In the imprecise decision tables, decision attribute values are specified imprecisely. Under such decision tables, lower and upper object sets for a set of decision attribute values are defined. Their properties are shown. Concepts of reducts of imprecise decision tables are studied. Disce...

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...