The Attribute Significance Measure And Applications Based On The Decision-making Table

Rough Set theory is a mathem atics tool for processing vague and imprecision knowledge, which is putted forward from Poland scientist Z.Pawlak. Rough Set theory has already been widely used in the area of complex system optimization, artificial intelligence and data mining etc. Attribute Reduction is the core content of the Rough Set theory research. It is a process for deleting the redundant attribute of the system on condition that it keeps the classification ability of the Decision-making System unchanged and the improved system keeps the function of the original Decision-making System. The Reasonable and effective algorithm of reduction is one of the main research contents of the theory of rough set.In the decision-making table, the different attributes have different significance, so there is a need to study the significance of the attributes. In order to solve the problem that the existing attributes significance measure methods usually ignore the interaction among the attributes, the paper presents a measure method based on difference degree. When given a set, the proposed method first divides it into several subsets according to the value of condition attributes, and then computes the difference degree in the subsets. Secondly, the important attributes are selected based on the value of difference degree. Further, for the problem of the fuzzy function, we construct the comprehensive evaluation model based on difference degree. Finally, we analyze the features of the proposed model with case study. The results show that the method has good operability and interpretability. Moreover, it can be applied in the fields such as machine learning, systems engineering and performance assessment.