Attribute Reduction of Relative Knowledge Granularity in Intuitionistic Fuzzy Ordered Decision Table

Binbin Sang, Xiaoyan Zhang, Weihua Xu


For the moment, the attribute reduction algorithm of relative knowledge granularity is very important research areas. It provides a new viewpoint to simplify feature set. Based on the decision information is unchanged, fast and accurate deletion of redundant attributes, which is the meaning of attribute reduction. Distinguishing ability of attribute sets can be well described by relative knowledge granularity in domain. Therefore, how to use the information based on relative knowledge granularity to simplify the calculation of attribute reduction. It is an important direction of research. For increasing productiveness and accuracy of attribute reduction, in this paper we investigate attribute reduction method of relative knowledge granularity in intuitionistic fuzzy ordered decision table(IFODT). More precisely, we redefine the granularity of knowledge and the relative knowledge granularity by ordered relation. And their relevant properties are proved. On the premise that the decision results remain unchanged, in order to accurately calculate the relative importance of any condition attributes about the decision attribute sets, the conditional attribute of internal and external significance are designed by relative knowledge granularity. And some important properties of relative attribute significance are proved. Therefore, we determine the importance of conditional attributes based on the size of the relative attribute significance. In the aspect of computation, the corresponding algorithm is designed and time complexity of algorithm is calculated. Moreover, the attribute reduction model of relative knowledge granularity of efficiency and accuracy is proved by test. Last, the validity of algorithm is demonstrated by an case about IFODT.

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