基于信息粒度与粗糙集的决策细化研究

从理论上研究了决策表中决策值细化程度与信息粒度、近似分类的精度及近似分类质量之间的关系,结果表明,决策属性的属性值划分得越细,则该属性的信息粒度、近似分类精度和近似分类质量的值就越小.仿真实验同时证明,在基于决策属性划分之下,对任意一个条件属性集经决策细化后的决策表所对应的信息粒度、近似分类精度和近似分类质量的值,都不大于决策细化前决策表所对应的信息粒度、近似分类精度和近似分类质量的值,这个结果对研究决策表属性约简和决策规则的有效性等问题都有指导作用.

Study on Decision Subdivision Based on Information Granularity and Rough Sets

Based on information granularity and rough set theory, the relations between the decision subdivision degree and the information granularity, the accuracy of approximation classification, and the quality of approximation classification were mainly discussed. It is theoretically demonstrated that for any condition attribute set, the finer the decision attribute value of a decision table is, the lower the information granularity, the accuracy of approximation classification, and the quality of approximation classification are. Simulation results show that the information granularities, the accuracy of approximation classification and the quality of approximation classification in the finer decision table are not bigger than the ones corresponding to the decision table before decision refinement. The research is helpful for the attribute reduction and for enhancing confidences of decision rules.