JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE) ›› 2021, Vol. 56 ›› Issue (2): 17-27.doi: 10.6040/j.issn.1671-9352.0.2020.236

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Optimal granularity selection based on minimum cost of extension domain change

LI Min1,2,3, YANG Ya-feng1,2,3, LEI Yu4, LI Li-hong1,2,3*   

  1. 1. College of Science, North China University of Science and Technology, Tangshan 063210, Hebei, China;
    2. Hebei Key Laboratory of Data Science and Application, Tangshan 063210, Hebei, China;
    3. Tangshan Key Laboratory of Data Science, Tangshan 063210, Hebei, China;
    4. College of Electrical Engineering, North China University of Science and Technology, Tangshan 063210, Hebei, China
  • Published:2021-01-21

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Abstract: Aiming at the problem that the cost of dynamic change of decision domain is seldom involved in the current optimal granularity selection algorithm, the extension set method is introduced, and the optimal granularity selection model based on the minimum cost of change of extension domain is proposed by combining the three-way decision. Firstly, the index grade discretization data table is determined by extension evaluation method, and the weight is used as the particle to carry out granulation, and the granular space is constructed by using binary relation crossover operator. Secondly, three domains are divided by fusing three decisions, and five domains of extension set are determined based on the dynamic changes of the three domains. Then, the measurement method of extension domain change is studied, the cost matrix is constructed, and the optimal granular layer is determined by the minimum cost of extension domain change. The model considers both static and dynamic characteristics comprehensively, and provides a new way to choose the optimal granularity. Finally, taking the data of water resources carrying capacity in Heilongjiang Province as an example, the validity of the model is verified, and the sensitivity analysis is carried out by using classification and regression trees. The results show that the model has good generalization.

Key words: granularity selection, three-way decision, extension set, change cost matrix, water resources carrying capacity

CLC Number: 

  • TP181
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