Decision rules acquisition based on interval knowledge granules for incomplete ordered decision information systems

摘要

For incomplete ordered decision information systems (IODIS), the interval, defined as an intersection of the dominating set of one object and the dominated set of another object, is regarded as the basic knowledge granule used for defining the lower and upper approximations. It is shown in this paper that such knowledge granule can help induce the "at least and at most" decision rules for IODIS, which would assign an object to more precise decision classes. In order to compute the optimal "at least and at most" decision rules, the concept of relative reduct of an interval is proposed, and the corresponding discemibility function is constructed for computing the relative reduct. Finally, an illustrative example is provided to demonstrate the advantages of our method in decision making.