Uncertainty Measurement and Attribute Reduction Algorithm Based on Kernel Similarity Rough Set Model

Baoguo Chen, Lei Chen, Ming Deng and Peijun Wang

Journal of Mathematics, 2022, vol. 2022, 1-13

Abstract: Attribute reduction is the core research content in rough set theory. At present, the attribute reduction of numerical information system mostly adopts the neighborhood rough set method. In order to further improve the similarity measurement effect between data objects, kernel function method is used to construct a new rough set model in numerical information system, and an uncertainty measurement method and attribute reduction method are proposed. Firstly, the similarity between objects of numerical information system is calculated by kernel function, and a granular structure model and rough set model based on kernel similarity relation are proposed. Then, from the perspective of kernel similarity rough approximation, an information system uncertainty measurement method called kernel approximation precision and kernel approximation roughness is proposed. Because these two measurement methods do not meet the strict monotonicity of information granulation, the concept of kernel knowledge granularity based on kernel similarity granular structure is further proposed in this paper. By combining kernel approximation precision and kernel approximation roughness with kernel knowledge granularity, an uncertainty measurement method of kernel similarity combination measurement is proposed. Finally, using the strict monotonicity of kernel similarity combination measurement, an attribute reduction algorithm for numerical information system is designed. Experimental analysis shows the effectiveness and superiority of the proposed method.

Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:hin:jjmath:5675200

DOI: 10.1155/2022/5675200

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