In this paper, we present a new approximate method in incomplete information systems. We extend the semantics of the unavailable values in incomplete information systems. By applying the variable precision rough set model to the attribute sets of objects, the unavailable values are taken into account quantitatively under independently relative distribution hypothesis while establishing the approximate space in the incomplete information systems. We show how to shrink the search space based on variable precision rough set model via two-level approximation: space approximation and set approximation.
Granular Computing, 2006 IEEE International Conference on; 06/2006
Due to the discarded attributes, the effectual condition classes of the decision rules are highly different. To provide a
unified evaluative measure, the derivation of each rule is depicted by the reduced attributes with a layered manner. Therefore,
the inconsistency is divided into two primary categories in terms of the reduced attributes. We introduce the notion of joint
membership function wrt. the effectual joint attributes, and a classification method extended from the default decision generation
framework is proposed to handle the inconsistency.
Intelligent Data Engineering and Automated Learning - IDEAL 2006, 7th International Conference, Burgos, Spain, September 20-23, 2006, Proceedings; 01/2006
Intelligent Information Processing III, IFIP TC12 International Conference on Intelligent Information Processing (IIP 2006), September 20-23, Adelaide, Australia; 01/2006