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Uncertainty Measure for Multisource Intuitionistic Fuzzy Information System

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  • Hong Wang
  • Hong Li
  • Zhen Zhang

Abstract

Multisource information systems and multigranulation intuitionistic fuzzy rough sets are important extended types of Pawlak’s classical rough set model. Multigranulation intuitionistic fuzzy rough sets have been investigated in depth in recent years. However, few studies have considered this combination of multisource information systems and intuitionistic fuzzy rough sets. In this paper, we give the uncertainty measure for multisource intuitionistic fuzzy information system. Against the background of multisource intuitionistic fuzzy information system, each information source is regarded as a granularity level. Considering the different importance of information sources, we assign different weights to them. Firstly, the paper proposes an optimal source selection method. Secondly, we study the weighted generalized, weighted optimistic, and weighted pessimistic multigranularity intuitionistic fuzzy rough set models and uncertainty measurement methods in the multisource intuitionistic fuzzy information system, and we further study the relationship between the three models and related properties. Finally, an example is given to verify the validity of the models and methods.

Suggested Citation

  • Hong Wang & Hong Li & Zhen Zhang, 2022. "Uncertainty Measure for Multisource Intuitionistic Fuzzy Information System," Complexity, Hindawi, vol. 2022, pages 1-21, April.
    • Handle: RePEc:hin:complx:3605881
    DOI: 10.1155/2022/3605881
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