Drawing conclusions from data?The rough set way

Abstract

In the rough set theory with every decision rule two conditional probabilities, called certainty and coverage factors, are associated. These two factors are closely related with the lower and the upper approximation of a set, basic notions of rough set theory. It is shown that these two factors satisfy the Bayes' theorem. The Bayes' theorem in our case simply shows some relationship in the data, without referring to prior and posterior probabilities intrinsically associated with Bayesian inference in our case and can be used to “inverse” decision rules, i.e., to find reasons (explanation) for decisions. © 2001 John Wiley & Sons, Inc.