不完备系统中基于非对称相似关系的粗集多属性决策

Based on Non-symmetric Similarity Relation of Rough Set Multi-attribute Decision Making

粗糙集理论在多属性决策、数据挖掘、机器学习、人工智能等领域发挥着越来越大的作用。经典粗糙集理论主要利用不可分辨关系对完备信息系统形成的划分来定义知识的上、下近似集,并在此基础上进行知识约简,规则推理和决策。而现实生活中存在大量不完备信息系统。针对不完备信息系统的多属性决策问题,从非对称相似关系的角度提出基于粗集的多属性决策方法。

The rough set theory plays an important role in such domains as multi-attribute decision making, data mining, machine learning and artificial intelligence, etc. According to the theory of classical rough set, the partition of complete information system classified by the indiscernibility relation is used to define the upper and lower approximation set for knowledge reduction, rule reasoning and decision making. While there are a large number of incomplete information systems in real life, According to incomplete information system of multiple attribute decision making problems proposed muhi-attribute decision making based on rough set from the perspective of the non-symmetric similarity relation.