Computer Science ›› 2018, Vol. 45 ›› Issue (2): 152-156.doi: 10.11896/j.issn.1002-137X.2018.02.027

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Optimal Granularity Selection of Attribute Reductions in Multi-granularity Decision System

SHI Jin-ling, ZHANG Qian-qian and XU Jiu-cheng   

  • Online:2018-02-15 Published:2018-11-13

Abstract: Granular computing,as an important theory method of artificial intelligent,studies the solution of uncertain,imprecise issues or complicated problems from different angles and granularity levels.On the basis of decision system theory of multi-granularity,information granulation and granularity partition were analyzed through different granularity levels.Then the concepts of granulating measurement and granular roughness which can exactly express the size of different granularity partition were defined for the problems of attribute reductions and efficient decision making in decision system.After discussing the local reduction method based on objects,an algorithm of optimal granularity reductions was proposed based on both universe and objects for overcoming the drawbacks of decision system reductions in traditional methods,which are only focused on the universe of decision system.Finally,the experimental results show the validity of the proposed algorithm.

Key words: Multi-granularity,Optimal granularity,Decision systems,Granulating measurement,Local reduction

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