经典的知识依赖性及属性重要性度量的新注记

知识依赖性及属性重要性度量是粗糙集的重要概念,广泛应用于知识约简和规则提取等方面。经典的知识依赖性及属性重要性度量在处理数据方面有局限性,有时无法得到较为精确、合理的度量结果,从而导致后续应用中得到的结果出现一系列的偏差。因此,通过深度分析经典知识依赖性,结合多数包含关系,并加入可信系数,提出了一种新的知识依赖性及属性重要性度量方法。最后,将新度量方法应用于一个决策信息系统,分析结果表明新度量方法是有效的。

New Note on Classical Measure of Knowledge Dependency and Attribute Significance

Measure of knowledge dependency and attribute significance is an important issue in rough set theory.It has been widely applied to knowledge reduction,rule extraction,etc.Classical measure of knowledge dependency and attri-bute significance has a littie bit of limitation in dealing with data,and sometimes it cannot obtain precise and reasonable results,which leads to a series of deviations in the subsequent applications.To this end,through deep analysis of the exi-sting classical knowledge dependency,combining it with the majority inclusion relation,and adding credibility parameters,a new method of measuring knowledge dependency and attribute significance was proposed.Finally,the new mea-sure method was applied to decision information system.And the analysis results show that the new method is effective.