Comparison Of Two Models Of Probabilistic Rough Sets

To generalize the classical rough set model, several proposals have been made by considering probabilistic information. Each of the proposed probabilistic models uses three regions for approximating a concept. Although the three regions are similar in form, they have different semantics and therefore are appropriate for different applications. In this paper, we present a comparative study of a decision-theoretic rough set model and a confirmation-theoretic rough set model. We argue that the former deals with drawing conclusions based on available evidence and the latter concerns evaluating difference pieces of evidence. By considering both models, we can obtain a more comprehensive understanding of probabilistic rough sets.