Rough Set Model Based On Sugeno Measure

Probabilistic rough set model studies on rough set theory from the viewpoint of probability theory. It has a wide range of application in uncertain information system. However, probabilistic rough set model is based on the probability measure, which is a class of non-negative set function satisfying countable additivity. Because the condition of additivity is too harsh while the equivalence relation in probabilistic rough set model too strict, the application range of probabilistic rough set models are limited. Considering the existence of many non-probability measures (e.g. Sugeno measure, credibility measure, uncertain measure) and general binary relation in practical applications, the sugeno measure based rough set and variable precision rough set models together with their counterparts in general relation are proposed on the basis of sugeno measure and variable precision rough set models. Moreover, the properties underlying the unions, intersections, and complements of those upper-approximations and lower-approximations are provided. Furthermore, the decision-making reasoning in information system which is based on the sugeno measure is implemented.