Dominance-Based Rough Set Approach to Granular Computing

Abstract

Dominance-based Rough Set Approach (DRSA) was introduced as a generalization of the rough set approach for reasoning about preferences. While data describing preferences are ordinal by the nature of decision problems they concern, the ordering of data is also important in many other problems of data analysis. Even when the ordering seems irrelevant, the presence or the absence of a property (possibly graded or fuzzy) has an ordinal interpretation. Since any granulation of information is based on analysis of properties, DRSA can be seen as a general framework for granular computing. After recalling basic concepts of DRSA, the article presents their extensions in the fuzzy context and in the probabilistic setting. This permits to define the rough approximation of a fuzzy set, which is the core of the subject. The article continues with presentation of DRSA for case-based reasoning, where granular computing based on DRSA has been successfully applied. Moreover, some basic formal properties of the whole approach are presented in terms of several algebras modeling the logic of DRSA. Finally, it is shown how the bipolar generalized approximation space, being an abstraction of the standard way to deal with roughness within DRSA, can be induced from one of the algebras modeling the logic of DRSA, the bipolar Brower-Zadeh lattice.