Computer Engineering and Applications ›› 2020, Vol. 56 ›› Issue (18): 214-220.DOI: 10.3778/j.issn.1002-8331.1906-0379

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Multi-granulation Decision-Theoretic Rough Set Method Based on Supervisory Mechanism and Its Application

LUO Gongzhi, MEI Tao   

  1. School of Management, Nanjing University of Posts and Telecommunications, Nanjing 210003, China
  • Online:2020-09-15 Published:2020-09-10



  1. 南京邮电大学 管理学院,南京 210003


To compensate for the deficiency of multi-granularity decision rough sets in dealing with uncertain information, this paper proposes a multi-granulation decision-theoretic rough set analysis method based on supervised mechanism. This method introduces intra-class and inter-class in multi-granularity decision rough sets in view of the advantages of supervised learning, that is, being able to involve the existing or predicted category label information of the object. The lower and upper approximations of the model are provided. Relevant properties and conclusions are proved. The effectiveness and reliability of the method are verified by the case of construction site project construction. The experimental results show that by adjusting two kinds of thresholds, the fault tolerance and classification ability of the revised version can be further improved.

Key words: supervisory mechanism, multi-granulation decision-theoretic rough set, probability rough set



关键词: 监督机制, 多粒度决策粗糙集, 概率粗糙集