Abstract: The attribute significance degree based on the knowledge base of rough sets shows the classification change after removing one or more attributes. For the inadequacy of the method to determine the attribute significance degree, the direct and indirect effects of the condition attributes acted on the decision attributes are considered, and the method to determine the attribute significance degree, which is based on the attribute dependency degree in rough sets, is improved. Moreover, after comparing the difference between the original and the improved method, the meaning of the later is discussed, and theorems show that the improved method is more credible. Finally, the simulation on the attribute significance degree of mechanical faults shows that the attribute significance degrees are more distinguishable and suitable for the attribute reduction, which are beneficial to decision-makers’ quick judgments.

Key words: rough set, attribute significance degree, fault diagnosis, attribute reduction, decision

摘要： 粗糙集理论知识库的属性重要度，体现的是去掉某个或某些属性前后的知识库分类变化的程度。对现有粗糙集理论的属性重要度确立方法的不足，充分考虑条件属性对决策的直接和间接的影响，提出一种新的基于粗糙集属性依赖度的属性重要度确定方法。此外，针对原有属性重要度与改进重要度的差别，讨论改进的属性重要度的意义，并证明改进的属性重要度更加可信。最后，利用改进的方法对机械故障属性重要度进行仿真；对比原有属性重要度的数据，改进方法获得的数据不但更符合属性约简结果，并且具有更大区分度，十分有利于决策者快速做出判断。

关键词: 粗糙集, 属性重要度, 故障诊断, 属性约简, 决策