Maximum Consistent Block Based Variable Precision Rough Set Model and Attribute Reduction

In this paper, attribute reduction of incomplete information system is studied. Firstly, optimistic and pessimistic generalized variable precision rough set models based on maximal consistent blocks are constructed. The relationship between the two models and their main properties are analyzed. After that β-lower optimistic (pessimistic) and β-upper distribution attribute reduction is defined, and the corresponding judgement theorem is given. Boolean method of attribute reduction is obtained, and it can keep the upper (lower) approximation distribution of the decision class unchanged. This method of constructing discernibility set between maximal consistent blocks reduces the size of the discernibility matrix, and the process of computing attribute reduction is simplified, which can effectively save computing time and storage space. Then two examples of incomplete information systems with “lost” “don't care” values and only “don't care” values are employed to illustrate the proposed method. Finally, 5 sets of incomplete information data sets from UCI data set are used to validate its effectiveness.

Abstract licence: CC BY-NC

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