Variable Precision Rough Sets Model Based on Similarity Degree in Incomplete Information Systems
不完备信息系统中基于相似度的变精度粗糙集模型

Knowledge reduction is one of the most important tasks in rough set theory,and most types of reductions in this area are based on complete information systems. However,many information systems are incomplete and some data are inaccurate in real world.In this paper,a new rough set model based on similarity degree is presented in incomplete information systems.This model extends the classical rough set model in complete information systems.Two heuristic algorithms based on the number of missing values and significance of attributes are proposed.Finally,an example is given to illustrate the validity of the presented algorithms.