Abstract
In this paper, we propose a heuristic algorithm to extract decision rules based on variable precision rough set models (VPRS models). The VPRS models provides a theoretical basis of regarding probabilistic / inconsistent information in the framework of rough set theory. The main idea of our algorithm is based on construction of suitable $\beta$-lower approximations by giving up to discern some discernible objects that belong to different decision classes each other. All decision rules extracted by our algorithm are guaranteed that the certainty of all extracted decision rules are equal to or higher than the predefined threshold of certainty.
