Uncertainty Measurement and Attribute Reduction Algorithm Based on Kernel Similarity Rough Set Model
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 use...
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| Format: | Article |
| Language: | English |
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Wiley
2022-01-01
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| Series: | Journal of Mathematics |
| Online Access: | http://dx.doi.org/10.1155/2022/5675200 |
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| author | Baoguo Chen Lei Chen Ming Deng |
| author_facet | Baoguo Chen Lei Chen Ming Deng |
| author_sort | Baoguo Chen |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-c432f87d514c4dc195f626f446ddd5c5 |
| institution | Kabale University |
| issn | 2314-4785 |
| language | English |
| publishDate | 2022-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Mathematics |
| spelling | doaj-art-c432f87d514c4dc195f626f446ddd5c52025-02-03T01:02:29ZengWileyJournal of Mathematics2314-47852022-01-01202210.1155/2022/5675200Uncertainty Measurement and Attribute Reduction Algorithm Based on Kernel Similarity Rough Set ModelBaoguo Chen0Lei Chen1Ming Deng2Department of Computer ScienceDepartment of Computer ScienceDepartment of Computer ScienceAttribute 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.http://dx.doi.org/10.1155/2022/5675200 |
| spellingShingle | Baoguo Chen Lei Chen Ming Deng Uncertainty Measurement and Attribute Reduction Algorithm Based on Kernel Similarity Rough Set Model Journal of Mathematics |
| title | Uncertainty Measurement and Attribute Reduction Algorithm Based on Kernel Similarity Rough Set Model |
| title_full | Uncertainty Measurement and Attribute Reduction Algorithm Based on Kernel Similarity Rough Set Model |
| title_fullStr | Uncertainty Measurement and Attribute Reduction Algorithm Based on Kernel Similarity Rough Set Model |
| title_full_unstemmed | Uncertainty Measurement and Attribute Reduction Algorithm Based on Kernel Similarity Rough Set Model |
| title_short | Uncertainty Measurement and Attribute Reduction Algorithm Based on Kernel Similarity Rough Set Model |
| title_sort | uncertainty measurement and attribute reduction algorithm based on kernel similarity rough set model |
| url | http://dx.doi.org/10.1155/2022/5675200 |
| work_keys_str_mv | AT baoguochen uncertaintymeasurementandattributereductionalgorithmbasedonkernelsimilarityroughsetmodel AT leichen uncertaintymeasurementandattributereductionalgorithmbasedonkernelsimilarityroughsetmodel AT mingdeng uncertaintymeasurementandattributereductionalgorithmbasedonkernelsimilarityroughsetmodel |