Uncertainty Measure Based on Rough Set in Information Systems

Abstract

Abstract. Since Shannon put forward information entropy and used it to measure the amount of information in the information system, people began to explore various new methods to measure the uncertainty in the information system. Rough set is a method to solve uncertainty problems, and the measurement of knowledge uncertainty is an important content in the research of rough set theory. Many scholars have explored this from different perspectives. This paper analyzes the uncertainty of knowledge from a new perspective based on Pawlak's definition of the degree of knowledge uncertainty contained in approximate sets, and provides a new measure of knowledge uncertainty. Compared to existing similar measures, this measure not only better reflects the connotation of knowledge uncertainty in the approximation space described by Pawlak, but also is computationally feasible. The research helps people better understand the causes of uncertainty in approximation spaces, and expands and enhances the applicability of rough set theory.