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| Attribute Reduction in Multi-granularity Formal Decision Contexts |
| LI Jinhai1,2, ZHOU Xinran1,2 |
1. Data Science Research Center, Kunming University of Science and Technology, Kunming 650500;
2. Faculty of Science, Kunming University of Science and Technology, Kunming 650500 |
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Abstract The existing attribute reduction methods for formal decision contexts cannot deal with multi-granularity data. Therefore, three attribute reduction methods are put forward in multi-granularity formal decision contexts to realize attribute reduction of an information system by removing the class-attribute blocks from the same category under each consistent granularity layer. Firstly, from the perspective of information granules, information entropy and conditional information entropy of the consistent granularity layer are introduced in the multi-granularity formal decision contexts to further measure the significance of attributes. Secondly, based on the average conditional information entropy and conditional information entropy in the coarsest and finest consistent formal decision contexts, the consistent granularity attribute reduction method and the coarsest and finest consistent granularity attribute reduction methods are proposed in multi-granularity formal decision contexts, and their corresponding implementation algorithms are developed. Finally, the experimental results show that the proposed attribute reduction methods are effective. In addition, it is concluded that the constraint of the consistent granularity attribute reduction method is too strict. Instead, the constraints of the coarsest and finest consistent granularity attribute reduction methods are relatively weaker.
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Received: 10 January 2022
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| Fund:National Natural Science Foundation of China(No.11971211,12171388) |
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Corresponding Authors:
LI Jinhai, Ph.D., professor. His research interests include cognitive computing, granular computing, big data analysis, concept lattice and rough set.
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| About author:: ZHOU Xinran, master student. Her research interests include formal concept analysis, granular computing and rough set. |
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2022, Vol. 35 
