Computer Science ›› 2017, Vol. 44 ›› Issue (Z6): 70-74.doi: 10.11896/j.issn.1002-137X.2017.6A.014

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Attribute Reduction Based on Variable Precision Rough Sets and Concentration Boolean Matrix

LI Yan, GUO Na-na and ZHAO Hao   

  • Online:2017-12-01 Published:2018-12-01

Abstract: Attribute reduction is the most important research topic in rough set theory.The traditional attribute reduction based on discer-nibility matrix can only handle consistent decision tables.Then the concept of improved discernibility matrix was proposed to effectively deal with both consistent and inconsistent decision tables.Further,the condensed Boolean matrix was defined to represent the discernibility matrix in order to save the storage space and improve the efficiency of matrix generation.Based on the previous work,the idea of variable precision was used to select some inconsistent objects in the developing of the discernibility matrix,thus more information can be considered in generating attri-bute reduction.The experimental results show that the proposed method performs advantages in both running speed and classification accuracy.

Key words: Rough set,Discernibility matrices,Attribute reduction,Concentration boolean matrix,Variable precision

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